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JUN 2006 



00-00-2006 to 00-00-2006 


CrossTalk: The Journal of Defense Software Engineering. Volume 19, 
Number 6, June 2006 









OO-ALC/MASE,6022 Fir Ave,Hill AFB,UT,84056-5820 






Approved for public release; distribution unlimited 















Same as 


Report (SAR) 

19a. NAME OF 

Standard Form 298 (Rev. 8-98) 

Prescribed by ANSI Std Z39-18 

^^3p r °j ects Fail 

4 Social and Technical Reasons for Software Project 

Applying a careful program of risk analysis and risk abatement can 
lower the effects of the technical and social issues that handicap 
projects and lower the probability of major software disasters. 
by Capers Jones 

What We’ve Got Here Is ... Failure to Communicate 

The failure to communicate is the root problem of more program 
failures than we allow ourselves to believe. This article uses the famous 
line from the movie Cool Hand Cuke , “What we’ve got here is ... failure to 
communicate,” to illustrate communication failures and successes. 
by Alan C. Jost 

Knowledge: The Core Problem of Project Failure 

This author contends that knowledge is the most common cause of 
project failures: either project managers do not have enough of it, or 
they are not using the knowledge they do have correctly. 
by Timothy K. Perkins 

^jjtgineering Technology 

Start With “Simple” Earned Value on All Your Projects 

The authors show that by only implementing 10 of the 32 American 
National Standards Institute/Electronic Industries Alliance’s criteria to 
all projects. Earned Value Management can be achieved. 
by Quentin W. Tleming and Joel M. Koppelman 


Statistical Methods Applied to EVM:The Next Frontier 

Earned Value Management (EVM) has brought science to management 
projects, and this article describes the elements necessary for performing 
statistical analysis in association with EVM. 
by Walt Tip ke 

Defining Short and Usable Processes 

This article describes common problems with process documentation, 
discusses best practices for defining short and usable processes and 
procedures, describes success stories in real organizations, and provides 
some lessons learned. 
by Timothy G. Olson 

OQ Should Your Projects’ Leaders Be on Springer? 

^ S This article draws parallels between the outrageous events on the Jerry 
Springer Show and problems faced by process improvement programs. 

by Paul Kimmerly 


From the Sponsor 

Coming Events 
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provided by Janna Jensen 

31 BackTalk 

76 SMXG Kevin Stamey 

309 SMXG Randy Hill 

402 SMXG Diane Suchan 

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Publisher Brent Baxter 

Associate Publisher Elizabeth Starrett 

Managing Editor Kase Johnstun 

Associate Editor Chelene Fortier-Lozancich 

Article Coordinator Nicole Kentta 

Phone (801) 775-5555 

E-mail c 

Crosstalk Online 

CROSSTALK, The Journal of Defense Software 
Engineering is co-sponsored by the U.S.Air Force 
(USAF), the U.S. Department of Homeland Security 
(DHS), and the U.S. Navy (USN). USAF co-sponsors: 
Oklahoma City-Air Logistics Center (ALC) 76 
Software Maintenance Group (SMXG), Ogden-ALC 
309 SMXG, and Warner Robins-ALC 402 SMXG. 
DHS co-sponsor: National Cyber Security Division of 
the Office of Infrastructure Protection. USN co-spon¬ 
sor: Naval Air Systems Command. 

The USAF Software Technology Support 
Center (STSC) is the publisher of CROSSTALK, 
providing both editorial oversight and technical review 
of the journal. CROSSTALK’S mission is to encourage 
the engineering development of software to improve 
the reliability, sustainability, and responsiveness of our 
warfighting capability. 

Subscriptions: Send correspondence concerning 
subscriptions and changes of address to the following 
address.You may e-mail us or use the form on p. 15. 

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Article Submissions.We welcome articles of interest 
to the defense software community. Articles must be 
approved by the CrossTalk editorial board prior to 
publication. Please follow the Author Guidelines, avail¬ 
able at <>. 
CrossTalk does not pay for submissions. Articles 
published in CrossTalk remain the property of the 
authors and may be submitted to other publications. 

Reprints: Permission to reprint or post articles must 
be requested from the author or the copyright hold¬ 
er and coordinated with CrossTalk. 

Trademarks and Endorsements: This Department of 
Defense (DoD) journal is an authorized publication 
for members of the DoD. Contents of CrossTalk 
are not necessarily the official views of, or endorsed 
by, the U.S. government, the DoD, or the STSC. All 
product names referenced in this issue are trademarks 
of their companies. 

Coming Events: Please submit conferences, seminars, 
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least 90 days before registration. Mail or e-mail 
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Back Issues Available: Please phone or e-mail us to 
see if back issues are available free of charge. 

2 CrossTalk The Journal of Defense Software Engineering 

June 2006 

From the Sponsor 

Why Do Projects Fail? 

D on’t we all wish that there were a simple answer to this question? Despite the many 
opinions, nobody has a straightforward answer that really applies to all projects. 
The articles in this issue, and many other articles that I reviewed, point to two facts: First, 
everyone has a theory on why programs fail, and second, there is no singular cause that 
universally applies. It is, frankly, surprising that the variety of statistics and articles on 
project failures have little in common. There were, however, a few - just a few — com¬ 
mon denominators to project failures. But since I am a glass-is-half-full kind of guy, I 
would rather view this topic from the side of success. Although I suspect there are more, I only 
found three common denominators that were consistently mentioned in several articles that I read 
as key to a project’s success, requirements management being the first common denominator. 
Success was not just defined by well-documented technical requirements, but well-defined pro¬ 
grammatic requirements/thresholds. Most articles specifically note requirements creep as a chal¬ 
lenge for projects. The driving forces behind requirements creep exist in many forms, and every 
project should have a systems engineering strategy to manage requirements, which is a good lead 
to the second common denominator: risk management. Successful programs not only identified 
risk early on, but specific steps were defined for managing the risk once identified. The third com¬ 
mon denominator consistently mentioned was project planning. Without incredible luck, no pro¬ 
ject can be successful without realistic and thorough up-front planning. To state the obvious, the 
better the planning, the more likely the outcome will match the plan. 

Like most of you, I have my opinions on how to have a successful project. I would like to 
point you to what I view are some other critical elements of project success. Having served as 
both an acquirer and supplier of software, I can tell you that a key to success is sound systems 
engineering. Few projects can be truly successful if they do not take into account the many ten¬ 
tacles that are linked to a project. Suppliers and acquirers must work together to implement good 
systems engineering. Another element critical to a project’s success is careful consideration in the 
planning phase to the availability of resources. The availability of key people, equipment, facili¬ 
ties, and many other project resources must be taken into account before committing to a sched¬ 
ule. One final element that the 76th Software Maintenance Group (SMXG) has found to be key 
to a project’s success is project management. Managers must adequately manage day-to-day activ¬ 
ities, ensure processes are followed, monitor progress, and communicate with the customer. 

While I do not mean to imply that these three elements are the only elements of successful 
projects, I do believe they are among the most critical elements of success. Our track record here 
at the 76th SMXG is evidence that these elements have certainly contributed to our success. Our 
325 software deliveries in the last 24 months have resulted in 100 percent on-time delivery per¬ 
formance. I hope you will take some of these lessons learned as well as the insights from this 
month’s authors and apply them to your project. 

We start this month with an article from Capers Jones on the social and technical reasons for 
project failure, followed by an entertaining article by Alan C. Jost on the importance of commu¬ 
nication. Timothy K. Perkins discusses the conclusions of his independent research and how lack 
of knowledge or the application of that knowledge can lead to project failure. Quentin W Fleming 
and Joel M. Koppelman suggest using a simplified version of earned value as a tool for project 
success. Our recently retired Walt Lipke continues to aid us with his article on statistical methods 
applied to Earned Value Management. Timothy G. Olson defines short and usable (emphasis 
added) processes. We conclude with Paul Kimmerly, who provides unique insight on how process 
improvement projects can actually help a project fail rather than help them succeed. 

I trust these articles will help in alerting you to warning signs of potential weaknesses that can 
lead to project failure. I also hope they help you and your team implement successful processes 
for project success. 

Kevin Stamey 

Oklahoma City Air Logistics Center, Co-Sponsor 

June 2006 3 

Why Projects Fail 

Social and Technical Reasons for Software Project Failures® 

Capers Jones 
Software Productivity Kesearch, LLC 

Major software projects have been troubling business activities for more than 50years. Of any known business activity, soft¬ 
ware projects have the highest probability of being cancelled or delayed. Once delivered, these projects display excessive error 
quantities and low levels of reliability. Both technical and social issues are associated with software project failures. Among 
the social issues that contribute to project failures are the rejections of accurate estimates and the forcing of projects to adhere 
to schedules that are essentially impossible. Among the technical issues that contribute to project failures are the lack of mod¬ 
ern estimating approaches and the failure to plan for requirements growth during development. However, it is not a law of 
nature that software projects will run late, be cancelled, or be unreliable after deployment. A careful program of risk analysis 
and risk abatement can lower the probability of a major software disaster. 

S oftware is an important but trou¬ 
bling technology. Software applica¬ 
tions are the driving force of modern 
business operations, but software is 
also viewed by many chief executives 
as one of the major problem areas 
faced by large corporations [1, 2, 3, 4]. 

The litany of senior executive com¬ 
plaints against software organizations 
is lengthy, but can be condensed down 
to a set of three very critical issues that 
occur over and over in hundreds of 

1. Software projects are not estimated or 
planned with acceptable accuracy 

2. Software project status reporting is 
often wrong and misleading. 

3. Software quality and reliability are 
often unacceptably poor. 

When software project managers 
(PMs) themselves are interviewed, they 
concur that the three major complaints 
levied against software projects are real 
and serious. However, from the point of 
view of software managers, corporate 
executives also contribute to software 
problems [5, 6]. The following are three 
complaints against top executives: 

1. Executives often reject accurate 
and conservative estimates. 

2. Executives apply harmful schedule 
pressure that damages quality. 

3. Executives add major new require¬ 
ments in mid-development. 
Corporate executives and software 
managers have somewhat divergent 
views as to why software problems are 
so prevalent. Both corporate execu¬ 
tives and software managers see the 
same issues, but these issues look quite 
different to each group. Let us exam¬ 
ine the root causes of the five software 
risk factors: 

1. Root causes of inaccurate estimat- 

© 2005-2006 by Capers Jones. All Rights Reserved. 

ing and schedule planning. 

2. Root causes of incorrect and opti¬ 
mistic status reporting. 

3. Root causes of unrealistic schedule 

4. Root causes of new and changing 
requirements during development. 

5. Root causes of inadequate quality 

“One advantage 
that function 
points bring to early 
estimation is that they 
are derived directly from 
the requirements and 
show the current status 
of requirements 

These five risk areas are all so critical 
that they must be controlled if large 
projects are likely to have a good 
chance of a successful outcome. 

Root Causes of Inaccurate 
Estimating and Schedule 

Since both corporate executives and 
software managers find estimating to 
be an area of high risk, what are the 
factors triggering software cost esti¬ 
mating problems? From analysis and 
discussions of estimating issues with 
several hundred managers and execu¬ 
tives in more than 75 companies 

between 1995 and 2006, the following 
were found to be the major root caus¬ 
es of cost estimating problems: 

1. Formal estimates are demanded 
before requirements are fully 

2. Historical data is seldom available 
for calibration of estimates. 

3. New requirements are added, but 
the original estimate cannot be 

4. Modern estimating tools are not 
always utilized on major software 

5. Conservative estimates may be 
overruled and replaced by aggres¬ 
sive estimates. 

The first of these estimating issues — 
formal estimates are demanded before require¬ 
ments are fully defined — is an endemic 
problem which has troubled the soft¬ 
ware community for more than 50 years 
[7, 8]. The problem of early estimation 
does not have a perfect solution as of 
2006, but there are some approaches 
that can reduce the risks to acceptable 

Several commercial software cost 
estimation tools have early estimation 
modes which can assist managers in 
sizing a project prior to full require¬ 
ments, and then in estimating develop¬ 
ment staffing needs, resources, sched¬ 
ules, costs, risk factors, and quality [9]. 
For very early estimates, risk analysis is 
a key task. 

These early estimates have confi¬ 
dence levels that initially will not be 
very high. As information becomes 
available and requirements are defined, 
the estimates will improve in accuracy, 
and the confidence levels will also 
improve. But make no mistake, soft¬ 
ware cost estimates performed prior to 
the full understanding of requirements 

4 CROSSTALK The Journal of Defense Software Engineering 

June 2006 

Social and Technical Reasons for Software Project Failures 

are intrinsically difficult. This is why 
early estimates should include contin¬ 
gencies for requirements changes and 
other downstream cost items. 

The second estimating issue — his¬ 
torical data is seldom available for calibra¬ 
tion of estimates — is strongly related to 
the first issue. Companies that lack his¬ 
torical information on staffs, sched¬ 
ules, resources, costs, and quality levels 
from similar projects are always at risk 
when it comes to software cost estima¬ 
tion. A good software measurement 
program pays handsome dividends 
over time [10]. 

For those organizations that lack 
internal historical data, it is possible to 
acquire external benchmark informa¬ 
tion from a number of consulting 
organizations. However, the volume of 
external benchmark data varies among 
industries, as do the supply sources. 

One advantage that function points 
bring to early estimation is that they 
are derived directly from the require¬ 
ments and show the current status of 
requirement completeness [11]. As 
new features are added, the function 
point total will go up accordingly. 
Indeed, even if features are removed 
or shifted to a subsequent release, the 
function point metric can handle this 
situation well [12, 13]. 

The third estimating issue — new 
requirements are added but the original esti¬ 
mate cannot be changed — is that of new 
and changing requirements without 
the option to change the original esti¬ 
mate. It is now known that the rate at 
which software requirements change 
runs between 1 percent and 3 percent 
per calendar month during the design 
and coding stages. Thus, for a project 
of 1,000 function points and an aver¬ 
age 2 percent per month creep during 
design and coding, new features sur¬ 
facing during design and coding will 
add about 12 percent to the final size 
of the application. This kind of infor¬ 
mation can and should be used to 
refine software cost estimates by 
including contingency costs for antici¬ 
pated requirements creep [14]. 

When requirements change, it is 
possible for some projects in some 
companies to revise the estimate to 
match the new set of requirements. 
This is as it should be. However, many 
projects are forced to attempt to 
accommodate new requirements with¬ 
out any added time or additional funds. 
I have been an expert witness in sever¬ 
al lawsuits where software vendors 
were directed by the clients to keep to 

contractual schedules and costs even 
though the clients added many new 
requirements in mid-development. 

The rate of requirements creep will 
be reduced if technologies such as 
joint application design (JAD), proto¬ 
typing, and requirements inspections 
are utilized. Here too, commercial esti¬ 
mating tools can adjust their estimates 
in response to the technologies that 
are planned for the project. 

The fourth estimating problem — 
modern estimating tools are not always utilised 
on major software projects — is the failure to 
use state-of-the-art software cost esti¬ 
mating methods. It is inappropriate to 
use rough manual rules of thumb for 
important projects. If the costs are 
likely to top $500,000 and the sched¬ 
ules take more than 12 calendar 
months, then formal estimates are 
much safer. 

“Several commercial 
software cost estimation 
tools have early 
estimation modes that 
can assist managers in 
sizing the projects prior 
to full requirements, and 
then in estimating 
development staffing 
needs, resources, 
schedules, costs, quality, 
and risk factors.” 

Some of the commercial software cost 
estimating tools used in 2006 include: 
COCOMO II, Construx Estimate, 

For large software projects in 
excess of 1,000 function points, any of 
these commercial software cost esti¬ 
mating tools can usually excel manual 
estimates in terms of accuracy, com¬ 
pleteness, and the ability to deal with 
tricky situations such as staffing 
buildups and growth rate in require¬ 

Estimating tools have one other 
major advantage: when new features 

are added or requirements change, 
redoing an estimate to accommodate 
the new data usually only takes a few 
minutes. In addition, these tools will 
track the history of changes made dur¬ 
ing development and, hence, provide a 
useful audit trail. 

The fifth and last of the major esti¬ 
mating issues — conservative estimates may 
be overruled and replaced by aggressive esti¬ 
mates — is the rejection of conservative 
or accurate cost estimates and devel¬ 
opment schedules by clients or top 
executives. The conservative estimates 
are replaced by more aggressive esti¬ 
mates that are based on business needs 
rather than on the capabilities of the 
team to deliver. For some government 
projects, schedules may be mandated 
by Congress or by some outside 
authority. There is no easy solution for 
such cases. 

The best solution for preventing 
the arbitrary replacement of accurate 
estimates is evaluating historical data 
from similar projects. While estimates 
themselves might be challenged, it is 
much less likely that historical data will 
be overruled. 

It is interesting that high-tech 
industries are usually somewhat more 
sophisticated in the use of estimating 
and planning tools than financial ser¬ 
vices organizations, insurance compa¬ 
nies, and general manufacturing and 
service groups. The high-tech indus¬ 
tries such as defense contractors, com¬ 
puter manufacturers, and telecommu¬ 
nication manufacturers need accurate 
cost estimates for their hardware prod¬ 
ucts, so they usually have estimating 
departments that are fully equipped 
with estimating tools that also use for¬ 
mal estimating methods [15]. 

Banks, insurance companies, and 
low-technology service companies do not 
have a long history of needing accu¬ 
rate cost estimates for hardware prod¬ 
ucts so they have a tendency to esti¬ 
mate using informal methods and also 
have a shortage of estimating tools 
available for software PMs. 

Root Causes of Incorrect 
and Optimistic Status 

One of the most common sources of 
friction between corporate executives 
and software managers is the social 
issue that software project status 
reports are not accurate or believable. 
In case after case, monthly status 
reports are optimistic that all is on 

June 2006 5 

Why Projects Fail 

schedule and under control until 
shortly before the planned delivery 
when it is suddenly revealed that 
everything was not under control and 
another six months may be needed. 

What has long been troubling 
about software project status reporting 
is the fact that this key activity is 
severely underreported in software 
management literature. It is also 
undersupported in terms of available 
tools and methods. 

The situation of ambiguous and 
inadequate status reporting was com¬ 
mon even in the days of the waterfall 
model of software development. 
Inaccurate reporting is even more 
common in the modern era where the 
spiral model and other alternatives such 
as agile methods and the object-oriented 
paradigm are supplanting traditional 
methods. The reason is that these non¬ 
linear software development methods 
do not have the same precision in 
completing milestones as did the older 
linear software methodologies. 

The root cause of inaccurate status 
reporting is that PMs are simply not 
trained to carry out this important 
activity. Surprisingly, neither universi¬ 
ties nor many in-house management 
training programs deal with status 

If a project is truly under control 
and on schedule, then the status 
reporting exercise will not be particu¬ 
larly time consuming. Perhaps it will 
take five to 20 minutes of work on the 
part of each component or depart¬ 
ment manager, and perhaps an hour to 
consolidate all the reports. 

But if a project is drifting out of 

control, then the status reports will 
feature redflag or warning sections that 
include the nature of the problem and 
the plan to bring the project back 
under control. Here, more time will be 
needed, but this is time very well 
spent. The basic rule of software sta¬ 
tus reporting can be summarized in 
one phrase: No surprises! 

The monthly status reports should 
consist of both quantitative data on 
topics such as current size and num¬ 
bers of defects and also qualitative 
data on topics such as problems 
encountered. Seven general kinds of 
information are reported in monthly 
status reports: 

1. Cost variances (quantitative). 

2. Schedule variances (quantitative). 

3. Size variances (quantitative). 

4. Defect removal variances (quantita¬ 
tive) . 

5. Defect variances (quantitative). 

6. Milestone completions (quantita¬ 
tive and qualitative). 

7. Problems encountered (quantita¬ 
tive and qualitative). 

Six of these seven reporting elements 
are largely quantitative, although there 
may also be explanations for why the 
variances occur and their significance. 

The most common reason for 
schedule slippage, cost overrun, and 
outright cancellation of a major sys¬ 
tem is that they contain too many bugs 
or defects to operate successfully. 
Therefore, a vital element of monthly 
status reporting is recording data on 
the actual number of bugs found com¬ 
pared to the anticipated number of 
bugs. Needless to say, this implies the 
existence of formal defect and quality 

estimation tools and methods. 

Not every software project needs 
the rigor of formal monthly status 
reporting. The following kinds of soft¬ 
ware need monthly status reports: 

• Projects whose total development 
costs are significant (>$1,000,000). 

• Projects whose total development 
schedule will exceed 12 calendar 

• Projects with significant strategic 
value to the enterprise. 

• Projects where the risk of slippage 
may be hazardous (such as defense 

• Projects with significant interest 
for top corporate management. 

• Projects created under contract 
with penalties for non-perfor¬ 

• Projects whose delivery date has 
been published or is important to 
the enterprise. 

The time and effort devoted to 
careful status reporting is one of the 
best software investments a company 
can make. This should not be a sur¬ 
prise: status reports have long been 
used for monitoring and controlling 
the construction of other kinds of 
complex engineering projects. 

During the past 20 years, a number 
of organizations and development 
approaches have included improved 
status reporting as a basic skill for 
PMs. Some of these include the 
Project Management Institute, the 
Software Engineering Institute’s (SEI) 
Capability Maturity Model® (CMM®), 
the reports associated with the Six 
Sigma quality methodology, and the 
kinds of data reported when utilizing 
International Organization for 
Standardization (ISO) Standards. 

Unfortunately, from examining the 
status reports of a number of projects 
that ended up in court for breach of con¬ 
tract, inaccurate status reporting still 
remains a major contributing factor to 
cost overruns, schedule overruns, and 
also to litigation if the project is being 
performed under contract. 

Root Causes of Unrealistic 
Schedule Pressures 

Unrealistic schedule pressure by exec¬ 
utives or clients is a common software 
risk factor. There are four root causes 
for unrealistic schedule pressure: 

1. Large software projects usually 

® Capability Maturity Model and CMM are registered in the 
U.S. Patent and Trademark Office by Carnegie Mellon 

Figure 1: Planned Versus Actual Schedules for Software Projects 

Planned Versus Actual Software Schedules 

— Planned 

- — Actual 

Size in Function Points (FP) 

6 CROSSTALK The Journal of Defense Software Engineering 

June 2006 

Social and Technical Reasons for Software Project Failures 

have long schedules of more than 
36 months. 

2. PMs are not able to successfully 
defend conservative estimates. 

3. Historical data from similar pro¬ 
jects is not available. 

4. Some kind of external business 
deadline affects the schedule. 

Figure 1 shows U.S. industry expe¬ 
riences derived from several thousand 
software projects. The upper curve 
shows the average delivery time in cal¬ 
endar months, while the lower curve 
shows the planned or desired delivery 
time. The larger the project, the 
greater the gap between the actual 
delivery date and the planned delivery 
date of the application [16]. 

Executives may be arbitrary in their 
decisions, but they are seldom stupid. 
While corporate executives might 
want a large software project finished 
in 24 months, they will almost certain¬ 
ly accept a 36-month schedule as a 
fact of life (if they know that not one 
of 50 similar projects within their 
industry has ever been completed in 
less than 36 months). Estimates might 
be overruled, but accurate historical 
data will probably keep schedule pres¬ 
sure from becoming unrealistic. 

The most difficult problem to solve 
is when some kind of external business 
deadline affects the project schedule. 
Unfortunately, these business deadlines 
are usually outside the control of either 
PMs or technical personnel. Examples 
of external business deadlines include 
contractual obligations, the starting 
dates of new laws that require software 
support, or some kind of technical sit¬ 
uation such as those associated with 
the Y2K problem. 

Such external fixed dates cannot be 
changed, or at least not changed by 
project personnel. Therefore a combi¬ 
nation of cutting back on functions, 
plus staff overtime, remains the most 
common method for dealing with 
fixed and unchanging delivery dates. If 
the mandated schedule is quite impos¬ 
sible to achieve, then a more drastic 
option would be project cancellation. 

Root Causes of New and 
Changing Requirements 
During Development 

The root causes of requirements 
changes are dynamic businesses. Real- 
world requirements for software must 
change in response to new business 
needs. However, average change rates 
of 2 percent per calendar month indi¬ 

cate that the methods used for gather¬ 
ing and analyzing the initial require¬ 
ments are inadequate and should be 

By counting function points from 
the original requirements and then 
counting again at the time of delivery, 
it has been found that the average rate 
of requirements growth is about 2 per¬ 
cent per calendar month from the 
nominal completion of the require¬ 
ments phase through the design and 
coding phases. 

The total accumulated volume of 
new or changing requirements can top 
50 percent of the initial requirements 
when function point totals at the 
requirements phase are compared to 

“More than 50 years 
of empirical studies 
have proven that 
projects with effective 
quality control cost 
less and have shorter 
schedules than similar 
projects with poor 
quality control. ,, 

function point totals at deployment. 
The state-of-the-art requirements 
change control includes the following: 

• A joint client/development change 
control board. 

• Use of JAD to minimize down¬ 
stream changes. 

• Use of formal prototypes to mini¬ 
mize downstream changes. 

• Formal review of all change 

• Revised cost and schedule esti¬ 
mates for all changes under 50 
function points. 

• Prioritization of change requests in 
terms of business impact. 

• Formal assignment of change 
requests to specific releases. 

• Use of automated change control 
tools with cross-reference capabili¬ 

One of the observed byproducts of 
the usage of formal JAD sessions is a 
reduction in downstream requirements 
changes. Rather than having unplanned 
requirements surface at a rate of 1 per¬ 

cent to 3 percent every month, studies 
of JAD by IBM and other companies 
have indicated that unplanned require¬ 
ments changes often drop below 1 
percent per month due to the effec¬ 
tiveness of the JAD technique. 

Prototypes are also helpful in 
reducing the rates of downstream 
requirements changes. Normally, key 
screens, inputs, and outputs are proto¬ 
typed so users have some hands-on 
experience with an example of the 
completed application. 

However, changes will always 
occur for large systems. It is not pos¬ 
sible to freeze the requirements of any 
real-world application.Therefore, lead¬ 
ing companies are ready and able to 
deal with changes and do not let them 
become impediments to progress; 
some form of iterative development is 
a logical necessity. 

Root Causes of Inadequate 
Quality Control 

Effective software quality control is 
the most important single factor that 
separates successful projects from 
delays and disasters. The reason for 
this success is that finding and fixing 
bugs is the most expensive cost ele¬ 
ment for large systems, and it takes 
more time than any other activity. 

The root cause for poor quality 
control is lack of solid empirical data 
on the cost effectiveness of a good 
quality control program. More than 50 
years of empirical studies have proven 
that projects with effective quality 
control cost less and have shorter 
schedules than similar projects with 
poor quality control. However, a dis¬ 
tressing number of PMs are not aware 
of the economics of quality control [5, 
10 ]. 

Successful quality control involves 
defect prevention, defect removal, and 
defect measurement activities. The 
phrase defect prevention includes all 
activities that minimize the probability 
of creating an error or defect in the 
first place. Examples of defect preven¬ 
tion activities include the use of the 
Six Sigma approach, the use of JAD 
for gathering requirements, the use of 
formal design methods, the use of 
structured coding techniques, and the 
use of libraries of proven reusable 

The phrase defect removal includes all 
activities that can find errors or 
defects in any kind of deliverable. 
Examples of defect removal activities 

June 2006 7 

Why Projects Fail 

include requirements inspections, 
design inspections, document inspec¬ 
tions, code inspections, and many 
kinds of testing [17, 18]. 

The phrase defect measurement 
includes measures of defects found 
during development and also defects 
reported by customers after release. 
These two key measures allow leading 
companies to calculate their defect 
removal efficiency rates, or the per¬ 
centages of defects found prior to 
release of software applications. 
Supplemental measures such as severi¬ 
ty levels, code complexity, and defect 
repair rates are also useful and important. 
Statistical analysis of defect origins and 
root-cause analysis are beneficial, along 
with the key measurements of cost and 
defect repairs [10]. 

Some activities benefit both defect 
prevention and defect removal simultane¬ 
ously. For example, participation in design 
and code inspection is very effective in 
terms of defect removal and also benefits 
defect prevention. Defect prevention is 
aided because inspection participants 
learn to avoid the kinds of errors that 
inspections detect. 

Successful quality control activities 
include defect prevention, defect removal, 
and defect measurements. The combina¬ 
tion of defect prevention and defect 
removal activities leads to some very sig¬ 
nificant differences in the overall numbers 
of software defects between successful 
and unsuccessful projects. 

For projects in the 10,000 function 
point range, the successful ones accumu¬ 
late development totals of around 3.0 
defects per function point and remove 
about 96 percent of them before cus¬ 
tomer delivery. In other words, the num¬ 
ber of delivered defects is about 0.12 
defects per function point or 1,200 total 
latent defects. Of these, about 10 percent 
— or 120 — would be fairly serious defects. 
The rest would be minor or cosmetic 

By contrast, the unsuccessful projects 
accumulate development totals of around 
7.0 defects per function point and remove 
only about 85 percent of them before 
delivery. The number of delivered defects 
is about 1.05 defects per function point or 
10,500 total latent defects. Of these, about 
15 percent — or 1,575 — would be fairly 
serious defects. This large number of seri¬ 
ous latent defects after delivery is very 
troubling for users. If a project has more 
than about 7.0 defects per function point 
and less than 85 percent removal efficien¬ 
cy, it will probably be cancelled because it 
can never successfully exit testing, and the 

test cycle will be hopelessly protracted. 

One of the reasons why successful 
projects have such a high defect removal 
efficiency compared to unsuccessful pro¬ 
jects is the use of design and code inspec¬ 
tions [17, 18]. Formal design and code 
inspections average about 65 percent effi¬ 
cient in finding defects. They also improve 
testing efficiency by providing better 
source material for constructing test cases. 

Unsuccessful projects typically omit 
design and code inspections and depend 
purely on testing. The omission of up¬ 
front inspections causes three serious 

1. The large number of defects still pre¬ 
sent when testing begins slows the 
project to a standstill. 

2. The badfixes x injection rate for projects 
without inspections is alarmingly high. 

“The most common 
reason for schedule 
slippages, cost 
overruns, and outright 
cancellation of major 
systems is that they 
contain too many bugs 
or defects to 
operate successfully/’ 

3. The overall defect removal efficiency 
associated with only testing is not suf¬ 
ficient to achieve defect removal rates 
higher than about 80 percent. 
Fortunately, the SEI, ISO quality stan¬ 
dards, and the Six Sigma approach have 
benefited quality control activities 
throughout the past 20 years. As a result, 
an increasing number of large projects 
have been successful compared to similar 
projects done in the 1980s. 

However, for very large projects above 
10,000 function points in size, missed 
delivery dates, cost overruns, and outright 
terminations remain distressingly high 
even in 2006. The industry is improving, 
but much more improvement is needed. 

Summary and Conclusions 

Large software projects are very haz¬ 
ardous business ventures. For projects 
above 10,000 function points, cancella¬ 
tions, delays, and cost overruns have been 
the norm rather than the exception. 

Careful analysis of the root causes of 
large software project delays and disasters 
indicate that most of the problems stem 
from inaccurate estimation, inaccurate sta¬ 
tus reporting, lack of historical data from 
similar projects, and suboptimal quality 

All of these root causes can be mini¬ 
mized or even eliminated by the adoption 
of formal estimating methods and tools, 
formal monthly status reports of both 
quantitative and qualitative data, collecting 
historical data, and improving quality con¬ 
trol methods. Large software projects will 
never be without risk, but if the risks can 
be brought down to acceptable levels, 
both clients and corporate executives will 
be pleased.^ 


1. The term bad fixes refers to secondary 
defects accidentally injected by means 
of a patch or defect repair that is itself 
flawed. The industry average is about 7 
percent, but for unsuccessful projects 
the number of bad fixes can approach 
20 percent; i.e. one out of every five 
defect repairs introduced fresh defects 
[14]. Successful projects, on the other 
hand, can have bad-fix injection rates 
of only 2 percent or less. 


1. Yourdon, Ed. Death March - The 
Complete Software Developer’s Guide 
to Surviving “Mission Impossible” 
Projects . Upper Saddle River, NJ: 
Prentice Hall, 1997. 

2. Glass, R.L. Software Runaways: 
Lessons Learned from Massive 
Software Project Failures . Prentice 
Hall, 1998. 

3. Johnson, James. “The Chaos Report.” 
West Yarmouth, MA: The Standish 
Group, 2000. 

4. Ewusi-Mensah, Kweku. Software 
Development Failures . Cambridge, 
MA: Massachusetts Institute of 
Technology Press, 2003. 

5. Jones, Capers. Assessment and 
Control of Software Risks . Prentice 
Hall PTR, 1994. 

6. Jones, Capers. Patterns of Software 

System Failure and Success . Boston, 
MA: International Thomson 

Computer Press, 1995. 

7. Boehm, Barry. Software Engineering 
Economics . Englewood Cliffs, NJ: 
Prentice Hall, 1981. 

8. Jones, Capers. “Sizing Up Software.” 
Scientific American Magazine Dec. 
1998: 104-111. 

9. Jones, Capers. Estimating Software 

8 CROSSTALK The Journal of Defense Software Engineering 

June 2006 

Social and Technical Reasons for Software Project Failures 

Costs . New York, NY: McGraw Hill, 

10. Kan, Stephen H. Metrics and Models 
in Software Quality Engineering . 2nd 
ed. Boston, MA: Addison-Wesley 
Professional, 2002. 

11. Jones, Capers. Applied Software 
Measurement . 2nd ed. New York, NY: 
McGraw Hill, 1996. 

12. Garmus, D. and D. Herron. Function 
Point Analysis - Measurement Prac¬ 

tices for Successful Software Projects . 
Boston, MA: Addison-Wesley Profes¬ 
sional, 2001. 

13. International Function Point Users 
Group (IFPUG). IT Measurement - 
Practical Advice from the Experts . 

Boston, MA: Addison-Wesley, 2002. 

14. Jones, Capers. Software Quality — 
Analysis and Guidelines for Success . 

Boston, MA: International Thomson 
Computer Press, 1997. 

15. Jones, Capers. Software Assessments. 
Benchmarks, and Best Practices . 

Boston, MA: Addison-Wesley 

Professional, 2000. 

16. Jones, Capers. Conflict and Litigation 
Between Software Clients and Devel¬ 

opers . Narragansett, R.I.: Software 
Productivity Research LLC, 2005. 

17. Radice, Ronald A. High Quality. Low 
Cost Software Inspections . Andover, 
MA: Paradoxicon Publishing, 2002. 

18. Wiegers, Karl E. Peer Reviews in 
Software — A Practical Guide . 

Boston, MA: Addison Wesley 

Professional, 2002. 

About the Author 

I rently the chairman of 

H ciates, LLC. He is also 
A the founder and former 

m l\ u ^airman of Software 
Productivity Research, LLC (SPR), 
where he holds the title of Chief 
Scientist Emeritus. He is a well-known 
author and international public speaker, 
and has authored the books “Patterns of 
Software Systems Failure and Success,” 
“Applied Software Measurement,” “Soft¬ 
ware Quality: Analysis and Guidelines 
for Success,” “Software Cost Esti¬ 
mation,” and “Software Assessments, 
Benchmarks, and Best Practices.” Jones 
and his colleagues from SPR have col¬ 
lected historical data from more than 600 
corporations and more than 30 govern¬ 
ment organizations. This historical data is 
a key resource for judging the effective¬ 
ness of software process improvement 
methods. The total volume of projects 
studied now exceeds 12,000. 

Software Productivity 
Research, LLC 
Phone: (877) 570-5459 

(973) 273-5829 
Fax: (781) 273-5176 

Coming Events 

July 5-7 

18 th International Conference on 
Software Engineering and Knowledge 
San Francisco, CA 06 .html 

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Software Engineering and Data 
Los Angeles, CA 

sede 06 /index.htm 

July 16-19 


The 10 th World Multi-Conference on 
Systemics, Cybernetics , and Informatics 
Orlando, FL 2006 /website/ 


July 16-19 

SERE 2006 

The 3 rd Symposium on Risk 
Management and Cyber-Informatics 
Orlando, FL 2006 /website/ 


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Minneapolis, MN 

www.agile 2006 .com 

July 24-28 

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10 th Annual Users' Group Conference 
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June 2006 9 

What We’ve Got Here Is ... Failure to Communicate 

Alan C. Jost 

Raytheon Company 

The tagline from Cool Hand Luke (1967) [1] has often been modified from its original. The Captain (Strother Martin) 
tells the recalcitrant chain gang prisoner Tuke (Paul Newman): “What we’ve got here is ... failure to communicate , ” not 
<( What we have here is a failure to communicate. ” We do not even quote the quote correctly. This article is a look at situa¬ 
tions where communication among team members is a critical factor in the potential failure of a program or its success. This 
is not a deep technical article, but l believe it is thought-provoking. When humans communicate in written, verbal, and non¬ 
verbalforms, many times the receiver misses the intended meaning. The “failure to communicate ” is the root cause for many 
program failures more times than we would admit or appreciate. 

T hroughout my career, I have experi¬ 
enced a number of program failures 
(even the term failure is relative and 
subject to a wide range of meanings 
depending on the individuals participat¬ 
ing in the discussion). These program 
failures can be directly related back to 
the basic tenet of this article: failure to 

Everyone reading this more than 
likely has had a similar experience and 
could add to the following situations. 
This article is not written as an indict¬ 
ment against any one individual, organi¬ 
zation, or program; it is written as a 
lighthearted look at how things that 
seem so simple can become major stum¬ 
bling blocks because of our failure to com¬ 
municate. But I do not want to just dwell 
on the failures, so a couple of good 
examples of how participants were able 
to communicate are also presented. 
Each of the situations is generalized by 
using groups as examples. 

NASA Mars Probe [2] 

One of the most dramatic failures of a 
project caused by failure to communi¬ 
cate was the NASA probe project in 
1999. The probe, the Mars Climate 
Orbiter, was to orbit Mars to gather cli¬ 
matic data. The Orbiter, at a cost of 
about $125 million, traveled more than 
400,000,000 miles to get to the planet. 
Upon arrival, the Orbiter entered an 
orbit 60 miles too low, and since it was 
not built to withstand the Mars atmos¬ 
phere, was destroyed. The design calcu¬ 
lations used to place the spacecraft into 
orbit were made in imperial measures in 
terms of pounds force. The software team, 
however, developed the burn control 
software using metric measurements 
and units in terms of newtons. While the 
error was less than 0.000015 percent, it 
was enough to be fatal to the mission. 
The communication error was only 
uncovered during the post-mortem of 

the failed mission. This was a major fail¬ 
ure to communicate between teams of 
intelligent, experienced professionals 
who did not check even the most obvi¬ 
ous items in the design and implementa¬ 
tion of the probe. “What we’ve got here 
is ... failure to communicate.” 

Radar Red Time 

In one situation in which I was person¬ 
ally involved, three organizations — the 
contractor, customer, and operational 

“One of the 
most dramatic failures 
of a project caused 
by a failure to 
communicate was the 
NASA probe project 
in 1999.” 

user — were collaborating to build a large 
radar system. The new radar was located 
near the old radar it was replacing. The 
old radar would not be decommissioned 
until the new radar was successfully 
operationally tested. In order to do this, 
maintenance red time of the old radar 
had to be scheduled when the new radar 
would be tested; this is where the three 
organizations failed to communicate. 
Through many planning meetings for 
red time, each group had a different 
interpretation of what exactly red time 
was. The meetings were productive and 
provided for a detailed operational test 
schedule. However, each organization 
had a different interpretation of the red 
time that created the resultant opera¬ 
tional test schedule. The failure to com¬ 
municate between the organizations was 

discovered at the first operational test 
event when the contractor requested 
that the old radar be turned off. 

The contractor assumed that red 
time meant the old radar would be 
turned off so they could test the new 
radar without interference from radia¬ 
tion being transmitted from the old 
radar. The customer assumed that red 
time meant that the old radar, while not 
turned off, would be placed in a mainte¬ 
nance state where the transmission of 
radiation would be rerouted through the 
wave-guides, eliminating a large portion 
of the ambient radiation. The opera¬ 
tional user’s version of red time meant 
that only the transmission lines for the 
radar data would be disconnected, so a 
false target would not be transmitted. 
Well, the reaction from the operational 
user was, “Turn the radar off!? The 
radar has never been turned off, and we 
don’t even know how to turn it off, and 
even worse, we don’t know how to turn 
it back on!” “What we’ve got here is ... 
failure to communicate.” 

At the heart of the situation was the 
klystron, the large tube that generated 
the radiation used to transmit the radar 
signal. Once turned on, it had not been 
turned off for years and there were no 
procedures to turn it off and back on 
again. In near real time, the three groups 
had to communicate with the klystron 
manufacturer to generate a procedure to 
minimize the energy and redirect the 
lower energy down the wave-guides. 
The new procedure did work, and the 
power down sequence was successfully 
repeated numerous times to support the 
operational testing of the new radar. 
Tailure to communicate the concept of red 
time among the participating organiza¬ 
tions could have lead directly to a major 
schedule impact on the program. It 
forced real-time communications 
between the participating organizations 
and manufacturer, resulting in the power 

I 0 CROSSTALK The Journal of Defense Software Engineering June 2006 

What We’ve Got Here Is ... Failure to Communicate 

down procedure. If the power down 
procedure failed, it would also have 
caused a major impact to the program. 
The procedure worked and the major 
schedule impact was avoided. What 
we’ve got here is ... communication! 

Contract Negotiations 

In another example, we have the cus¬ 
tomer and contractor negotiating the 
functionality included in the contractor’s 
proposal. During the negotiations, it 
was mentioned by the customer that 
they only had two-thirds of the pro¬ 
posed price in their budget. The con¬ 
tractor was requested to reduce their bid 
to match the customer’s budget and to 
eliminate the functionality needed to hit 
the target reduced-proposal price. The 
proposal team developed the new pro¬ 
posal with reduced functionality to meet 
the customer’s budget and provided the 
updated information to the negotiating 

Somehow, some way, the reduced 
functionality was not accurately commu¬ 
nicated to the customer. “What we’ve 
got here is ... failure to communicate.” 

It came to light at the first customer 
contractor system specification review 
when the software technical lead pre¬ 
sented the reduced functionality list. 
The reaction from the customer was not 
anticipated. Where were the missing 
functions? The ones that were eliminat¬ 
ed to reduce the bid were the functions 
they were asking about and the wheels 
started to fall off. Under the contract, 
the contractor had to develop the func¬ 
tionality directed by the customer, 
whether in the specification or not, and 
the contractor would have to recoup the 
costs through the country’s court sys¬ 
tem. Eventually, the program resulted in 
delivery of the system with the full 
functionality, which the customer 
assumed they were going to get for the 
reduced price that matched their budget. 
The extra functionality, however, 
required the contractor to fund the addi¬ 
tional work. In the end, the court sided 
with the contractor, and the customer 
ended up paying for the full functionali¬ 
ty by reimbursing the contractor for the 
additional funding. While eventually 
remedied, the initial failure to communicate 
made the entire program a contentious 
affair between customer and contractor. 

Communication Systems vs. 

These three situations indicate the 
importance of eliminating the failure to 

communicate among program team mem¬ 
bers. It is not that we do not have ade¬ 
quate communication systems to com¬ 
municate with, we have an overabun¬ 
dance of communication and collabora¬ 
tive systems: telephones, cell phones, 
walkie-talkies, blueberries, blackberries, 
e-mail, v-mail, fax, eRooms, Docushare, 
meeting rooms, Sametime (Lotus instant 
messaging and Web conferencing), and a 
multitude of other communication and 
collaboration systems. This is not the 
problem. The problem is the clear trans¬ 
mission of ideas and concepts between 
program team members that is at the 
heart of the problem. “What we’ve got 
here is ... failure to communicate.” 

As the reader, you probably have 
examples of programs where the com¬ 
munication among team members was 
very good and the project turned out to 
be a success. To see the impact of good 
communication leading to successful 
projects, I like to look to the television 
show The Apprentice. The projects on the 

“It was an 
overwhelming victory 
for the project 
manager who 
communicated with 
his potential customers. 
What weve got here 
is ... communication!” 

show are contrived to be completed in a 
short period of time to fit the presenta¬ 
tion of the project in a one-hour time 
slot. It is interesting to see that almost 
100 percent of the time the team that 
had good communication with their 
customer-judge, focus groups, and/or 
among the team members had the suc¬ 
cessful project. 

The Apprentice - Mural [3] 

In one project, two teams had to devel¬ 
op an advertising mural for a new elec¬ 
tronic game. The murals were to be 
done in Harlem. One team was led by a 
project manager who came from a 
neighborhood similar to Harlem and she 
knew what would be a good advertising 
mural. Since she knew what the customers 
would like, she knew how to create the 
mural to attract customers to buy the 

electronic game. The other team was led 
by a project manager who came from an 
upper-edge society and was kind of a 
geek; he immediately set out to get feed¬ 
back from a customer focus group in 
the neighborhood where the mural 
would be placed. He wanted to find out 
what was important to them as far as 
electronic games were concerned. He 
not only talked with the kids who would 
use the games, but with the parents who 
would ultimately purchase the games. 
Well, guess which team won the project? 
Which mural did a better job in selling 
the product? Was it the know-it-all from 
the ’hood, or was it the geek who com¬ 
municated with the people in the neigh¬ 
borhood focus group? It was an over¬ 
whelming victory for the project manag¬ 
er who communicated with his potential 
customers. What we’ve got here is ... 

The Apprentice - Solstice [4] 

A second The Apprentice project that 
demonstrated the importance of com¬ 
munication was the development of a 
sales brochure to describe the new 
Pontiac Solstice Roadster. One team was 
led by and consisted of all men who nat¬ 
urally knew exactly what it would take to 
sell the new two-seat, convertible road¬ 
ster. The other team was led by a 
woman, who, by her own admission, was 
not much into cars. The male-led team 
took the approach of making the car a 
macho-type of machine that would 
attract good-looking women to the car’s 
male driver, while the female-led team 
spoke with the General Motors repre¬ 
sentatives about how they wanted the 
car to be portrayed. Well, you do not 
have to be a wizard to guess who won 
this project management contest. The 
female-led team won because the pro¬ 
ject manager captured what the execu¬ 
tives communicated they wanted in the 
sales brochure. Even more importantly 
was that the Pontiac executives, who 
were also the judges of the two 
brochures, decided to use the brochure 
designed by the female-led team as the 
actual Solstice brochure in Pontiac 
showrooms across the nation. While the 
projects are somewhat contrived to sup¬ 
port the premise behind the show, they 
do demonstrate that the ability to com¬ 
municate is critical to the success of the 
project — any project involving a team of 
people attempting to accomplish a task. 

Apollo 13 - Recovery [S] 

The original Apollo 13 problem was 
caused when the number two oxygen 

June 2006 1 I 

Why Projects Fail 

tank in the service module exploded 
because of a short circuit in the oxygen 
tank that occurred during a routine stir¬ 
ring procedure. This problem was not 
the result of a failure to communicate. 
What I am using this dramatic mission 
failure example for is to demonstrate 
the success achieved with the ability of 
the NASA Apollo ground team to com¬ 
municate effectively, not only between 
themselves to develop solutions, but 
also to communicate those solutions to 
the Apollo 13 crew The initial explo¬ 
sion also caused the number one oxygen 
tank to fail and the fuel cells that sup¬ 
plied the command module with elec¬ 
tricity to have problems. In the initial 90 
minutes, it was brainstormed by the 
ground crew to use the Lunar Lander as 
a lifeboat for the crew. However, the 
Lunar Lander was designed to be used 
for 45 hours only, and the return mis¬ 
sion around the moon would take 90 
hours. There was plenty of oxygen with 
barely enough electrical power to make 
the return journey. The foreseeable 
problem was the eventual build up of 
carbon dioxide in the spacecrafts. There 
were enough lithium hydroxide canisters 
in the command module and Lunar 
Lander between them, but the com¬ 
mand module square canisters were not 
compatible with the round openings in 
the Lunar Lander module control sys¬ 
tem. The Houston mission control team 
gave the brainstorming team the materi¬ 
als available only to the Apollo 13 crew. 
The brainstorming team had to come 
up with the solution to the Apollo 13 
square-peg-in-a-round-hole problem. Once 
they came up with the solution, they had 
to communicate that solution to the 
crew to implement. Using plastic bags, 
tape, cardboard, and the square canis¬ 
ters themselves, the brainstorming team 
came up with the solution. They were 
able to communicate the solution to the 
crew in time for their implementation, 
and the rest is history. What we’ve got 
here is ... communication. 


Human-to-human communication is 
critical in managing programs. This is 
even recognized in the Capability 
Maturity Model® Integration where 
stakeholder involvement, reviews with 
higher levels of management, and other 
process areas (specific and generic prac¬ 
tices) are based on not failing to communi¬ 

I hear you was one of the most popu¬ 
lar phrases in the late ’90s. It generally 
translated as one person understood 

what the other person meant to say. 
While the words truly mean that you 
physically heard the words spoken, a 
more appropriate response would have 
been l understood you. I leave you with 
just two famous quotes. The first is a 
small, simple example of a failure to 
communicate, and the second is an 
excellent example of precise communi¬ 

In the movie Apollo 13 , astronaut Jim 
Lovell (Tom Hanks) tells Mission 
Control: “Houston, we have a prob¬ 
lem.” The line has often been misquot¬ 
ed as “Houston, we’ve got a problem.” 
The historical quote from Apollo 13’s 
Commander Jim Lovell was: “Houston, 
we’ve had a problem.” The actual his¬ 
toric exchange was the following (the 
times are in mission times in hours, 
minutes, and sections after launch) [5]: 

• 55:55:20 — Swigert: “Okay, Houston, 
we’ve had a problem here.” 

• 55:55:28 — Lousma: “This is Hous¬ 
ton. Say again please.” 

• 55:55:35 — Lovell: “Houston, we’ve 
had a problem. We’ve had a main B 
bus undervolt.” 

By now, it is readily apparent the 
importance of communication. So in 
conclusion, an example of precise com¬ 
munication is appropriate. Again, a bit 
contrived, but it makes the point. In the 
movie The Fugitive during the scene right 
after the train wreck where Dr. Richard 
Kimball (Harrison Ford) escapes, U.S. 
Marshal Samuel Gerard (Tommy Lee 
Jones) has to take over a just-formed, 
very large search team of local police 
who are extremely reluctant to be led by 
the Wyatt Earp-type marshal. He com¬ 
municates precisely what he needs done. 
In one short, memorable speech he 
states his requirements: 

Listen up, ladies and gentleman. 
Our fugitive has been on the run 
for 90 minutes. Average foot 
speed over uneven ground, bar¬ 
ring injury, is four miles an hour. 
That gives us a radius of six 

What I want out of each and 
every one of you is a hard target 
search of every gas station, resi¬ 
dence, warehouse, farmhouse, 
henhouse, outhouse, and dog 
house in that area. Checkpoints 
go up in 15 miles. Your fugitive’s 
name is Dr. Richard Kimball. Go 
get him!” [6] 

Any questions on how clear his com¬ 

munication was? In real estate, the most 
important thing is location, location, 
location. In program management it is 
communication, communication, com¬ 
munication. ♦ 


1. Cool Hand Luke . Dir. Stuart 
Rosenberg. Perf. Paul Newman, 
George Kennedy, J.D. Cannon, Lou 
Antonio, and Robert Drivas. Warner 
Brothers: 1967. 

2. Nickson, David, and Suzy Siddons. 
Project Disasters & How to Survive 
Them . London and Sterling, VA: 
Kogan Page, 2006. 

3. “The Writing On the Wall.” The 
Apprentice NBC. 24 Feb. 2005. 

4. “A Lovely Drive.” The Apprentice 
NBC. 14 Apr. 2005. 

5. Nickson, David, and Suzy Siddons. 
Project Disasters & How to Survive 
Them . London and Sterling, VA: 
Kogan Page, 2006. 

6. The Fugitive . Dir. Andrew Davis. Perf. 
Harrison Ford and Tommy Lee Jones. 
Warner Brothers: 1993. 

About the Author 

Alan Jost (Lt. Col., U.S. 
Air Force, retired) is a 

senior software program 
manager in the Raytheon 
Northeast Software En¬ 
gineering Center (SWEC) 
where he works multiple tasks: Software 
Engineering Process Group Executive 
Committee for SWEC’s Capability 
Maturity Model Integration SM Level 5 
sustainment, process engineer on the 
AutoTrac III Air Traffic Control Product 
Line, and DD(X) ExComms Software 
Cross Product Team. Jost joined 
Raytheon in 1991 after 20 years in the Air 
Force. He has served in a variety of line 
management, process engineering, and 
software task management roles in 
SWEC, and served intermittently as 
Software Engineering Process Group 
Chair between 1993 and 2001. 


Mail Stop 3-1-3914 
1001 Boston Post RD 
Marlborough, MAO 1752 
Phone: (508) 490-4282 
Fax: (508) 490-1366 

I 2 CROSSTALK The Journal of Defense Software Engineering 

June 2006 

Knowledge: The Core Problem of Project Failure 

Timothy K. Perkins 
Software Technology Support Center 

After having participated in more than 10 independent reviews of major acquisition projects\ and having been associated with 
projectjprogram management throughout my military career ; I assert that the cause of project failures is knowledge: either 
managers do not have the necessary knowledge , or they do not properly apply the knowledge they have. 

H aving led and participated in 
more than 10 Independent 
Expert Program Reviews 1 (IEPRs) for 
the Software Technology Support 
Center and the Tri-Service Assessment 
Office, and having spent my military 
career as a project/program manager, 
several individuals have asked if there 
is a common thread among programs 
or projects that are having difficulty. 
The answer is yes. Some expect the 
thread to be project planning, others 
risk management, and others expect 
one of the other project management 
themes. However, the root causes can 
be reduced to two issues: either pro¬ 
ject managers do not have the knowl¬ 
edge they need, or they do not proper¬ 
ly apply the knowledge they have. 
Throughout the remainder of this arti¬ 
cle, I will refer only to projects, but 
will mean both programs and projects. 

Some may consider these two 
issues to be too simplistic. However, if 
they take their pet principle from the 
Capability Maturity Model Integration 
(CMMI®) 2 , the Project Management 
Institute’s Project Management Body of 
Knowledge (PMBOK) 3 , the Tri-Service 
Assessment 4 , Typology, etc., and then 
ask why a project is having difficulty in 
that particular area, it still boils down 
to either a project manager lacks 
knowledge of the particular principle, 
or that the knowledge has not been 
applied properly. The Project Failure 
Cause-Effect Diagram (Figure 1 and 
sidebar) shows this relationship, 
admittedly through a great leap of 
logic between all the causes leading to 
project failure (155). 

Lack of Knowledge 

The first of these primary causes of 
project failure — project managers do 
not know what to do (115) — is the eas¬ 
iest to correct. One solution is to pro¬ 
vide the necessary training, remedying 
the problem of managers not receiving 
necessary training (100). Jack Ferguson, 

® CMMI is registered in the U.S. Patent and Trademark 
Office by Carnegie Mellon University. 

while teaching workshops for the 
Software Engineering Institute, used to 
use the phrase “Just too late training.” 
What he meant was that too often, 
training in a particular topic is provid¬ 
ed too far in advance of need. Students 
often have been heard saying, “Why do 
I need to learn this stuff? I’ll never use 
it.” When training is provided just too 
late , the students have already realized 
the need for the training and can read¬ 
ily see how the principles being taught 
can help them be more successful in 
accomplishing their projects. This does 
not mean that the project is in trouble 
prior to receiving training, but that 
training is provided at the appropriate 
time in the project life cycle. For exam¬ 

ple, providing in-depth training on 
project closeout prior to project initia¬ 
tion has less value than providing such 
training as the project enters the pro¬ 
ject closeout phase of the life cycle, 
and project members now realize the 
importance of having the training. 

This, however, implies that the pro¬ 
ject has a plan for training. Many orga¬ 
nizations that have undergone IEPRs 
had neither individual training plans 
nor an allocated budget to provide 
necessary training, implying that senior 
management had either not been 
trained in the need for establishing an 
organization training program or had 
not applied what they had been taught. 

The project manager’s lack of 

The Project Failure Cause-Effect Diagram 

The Cause-Effect Diagram is read by locating the entity at the tail of an arrow, and 
reading it preceded by the word // Then read the entity at the head of the arrow, pre¬ 
ceded by the word then . For example, the arrow between entities 100 and 115 of the 
figure would be read: //100 Project managers have not received necessary training, 
then 115 Project managers do not know what to do. If there are several causes joined 
by an ellipse, read the //only once, with other contributing cause statements joined by 
and. For example, the arrows between entities 110 and 155 and 130 and 155 read: // 
110 Project managers do not properly apply the knowledge they have and^O Project 
managers do not believe a project management principle adds value then 155 The 
project fails. 

Figure 1: The Project Tailure Cause-Effect Diagram 

^155 The Project Fails^ 



C 145 'N 

C 150 "N 


| Statutes prevent 

prevent project 

project managers from 

managers from doing 

1 doing what they know 

what they know they 
V should do. J 

they should do. J 

f 140 \ 

The project manager's | 
primary goal is other I 
k than project success. J 

r 135 \ 

Project managers 1 
believe a project 
management principle I 
y is flawed. ^ 

June 2006 13 

Why Projects Fail 

experience (105) is often regarded as a 
what came first, the chicken or the egg? problem. 
An individual needs experience to be a 
good manager, but how can one gain 
experience unless they are given the 
opportunity to manage? The answer is to 
allow managers to develop experience by 
learning to manage small projects before 
being given responsibility for large pro¬ 
jects. However, this is not always the case. 
Some project managers have been put in 
charge of acquisition category one (ACAT 
1) projects after having received only the 
14-week Defense Systems Management 
College program management course, 
having no prior experience in system 

Improper Application of 

Project managers not properly applying the 
knowledge they have (110) — the second 
root cause of project failure - is more dif¬ 
ficult to remedy. There are five associated 
causes and effects for this cause shown in 
Figure 1. If the issue is one of overlooking 
the implementation of a project manage¬ 
ment principle (120), i.e., just a lapse of 
memory in what needed to be done, then 
gentle reminders from subordinates, peers, 
or supervisors can be a catalyst to correct 
the omission. Program offices can provide 
mentoring to project managers to deal with 
such oversight. This mentoring can be 
used to provide refresher training to those 
who either have forgotten what to do or 
may not be familiar with current policy, 
directives, procedures, etc. 

Stories abound of projects tasked to 
accomplish the impossible based on 
imposed constraints (e.g, cost, resources, 
performance, etc.) (125). For example, the 
project manager may determine that a pro¬ 
ject will take 36 months to complete, but 
downward direction is to provide rubber on 
the ramp in 30 months. The only way to 
accomplish this is to take shortcuts, elimi¬ 
nating such things as peer reviews, close 
configuration management, risk manage¬ 
ment, etc. Unfortunately, taking shortcuts 
usually results in lengthening a project 
because of rework. What would have 
taken 36 months in a well-planned project 
now takes 48 months. The only real cure 
that I know of for this issue is personal 
integrity: the willingness to tell higher 
management that their tasking is impossi¬ 
ble, and then helping senior management 
realize that based on current technology, 
policy, directives, procedures, budget avail¬ 
ability, etc., that a project cannot be com¬ 
pleted as directed. Note that having a 
repository of measurements from previ¬ 

ous projects or using available industry 
data can help sway senior management in 
setting reasonable expectations. 

Some project managers consider some 
mandated project management practices 
to be of little value (130). For example, 
some consider preparing a formal project 
plan to be a waste of effort. They consid¬ 
er a project schedule prepared by using 
one of the popular project management 
software packages to be adequate 5 . Others 
may consider peer reviews of code or doc¬ 
uments to use more resources than the 
value gained. Rather than discuss their 
concerns with senior management, they 
choose to ignore the principle with which 
they disagree. When the belief that a prac- 

“Within the past few 
years, changes in the 
Federal Acquisition 
Regulation and the 
Department of Defense 
5000 series have given 
managers greater 
latitude in managing 
their projects, but the 
pendulum can always 
swing back to impose 
more constraints, 
whether they are 
warranted or not. ” 

tice does not add value is coupled with the 
decision to not implement it, project fail¬ 
ure can occur. Project managers should 
discuss their concerns with senior man¬ 
agers and resolve them. One of the best 
ways to deal with these concerns is to use 
historical data from other projects to vali¬ 
date the benefit of certain practices or to 
show the consequences of not following 
certain practices. 

Books espousing various project man¬ 
agement philosophies and methodologies 
abound. Current policies and directives 
have mandated some of the philosophies 
and methodologies while ignoring others. 
Some project managers may not believe 
that the mandated philosophies are the 

best approaches to use (135). Rather 
than implement what they consider to 
be flawed methods, they choose to fol¬ 
low what they consider to be proper 
principles. During management reviews, 
it becomes evident that the mandated 
methods are not being used, and the 
project manager is directed to imple¬ 
ment the mandated method. Resources 
are now consumed in either getting on 
the right track or in trying to gain 
approval to not use the mandated 
methodology — resources that are usual¬ 
ly scarce. This use of scarce resources 
has a negative impact on the chance of 
project completion within the originally 
allocated cost and schedule. One solu¬ 
tion to this problem is to gain agreement 
during the project initiation phase of the 
project life cycle among all parties 
regarding the project management prin¬ 
ciples to be used for the remainder of 
the project. 

But what if the application of sound 
project management principles to ensure 
project success is not the primary goal 
of the project manager (140)? While this 
problem seldom occurs, a few project 
managers may see their current position 
as a stepping stone for advancement to 
higher positions. Their goal may be to 
show good short-term results at the 
expense of overall project success. They 
build a house of cards hoping it will stay 
together and that the collapse will not 
occur until after the project manager is 
reassigned. This also is a personal 
integrity issue. The development of per¬ 
sonal integrity in project managers is 
well beyond the scope of this article. 

Though the issues of policy/direc¬ 
tives (145) or statutes (laws) (150) pre¬ 
venting a project manager from proper¬ 
ly managing a project seldom occur, they 
both must be recognized as potential 
causes of project failure. Within the past 
few years, changes in the Federal 
Acquisition Regulation and the 
Department of Defense (DoD) 5000 
series have given managers greater lati¬ 
tude in managing their projects, but the 
pendulum can always swing back to 
impose more constraints, whether they 
are warranted or not. 

A Word of Caution 

If projects continue to have difficulty 
even after applying what are considered 
to be sound project management princi¬ 
ples, maybe it is the principle that is in 
error. One definition of idiocy is to con¬ 
tinue do things the same way and to 
expect different results. However, doing 

I 4 CROSSTALK The Journal of Defense Software Engineering 

June 2006 

Knowledge:The Core Problem of Project Failure 

things the same way is an underlying 
premise of process improvement, i.e., 
the process must be consistent (in statis¬ 
tical control) before it can be improved. 
If project performance is not as desired, 
even after consistent application of the 
project management principle, the 
underlying principle should be analyzed 
to determine the reason for the continu¬ 
al shortfall. Perhaps the principle is not 
as sound as some would have you 


Think about known project failures then 
do a root cause analysis in your mind. For 
example, why wasn’t a project properly 
planned? Why was risk management not 
properly implemented? Why was the pro¬ 
ject not properly tracked? Why were any 
of the other project management princi¬ 
ples not properly followed? I believe that 
if you evaluate the potential causes, you 
will reach the same conclusion that I have: 
it all boils down to knowledge. Either indi¬ 
viduals do not have the necessary knowl¬ 
edge, or they have not properly applied 
the knowledge. ♦ 


1. IEPRs were called for in Interim 
Regulation, DoD 5000.2-R, January 1, 
2001, paragraph 2.6.8 for ACAT I-III 
Software Intensive Programs using the 
words, “The acquisition strategy shall 
describe the planned use of indepen¬ 
dent expert reviews for all ACAT I 
through ACAT III software-intensive 
programs.” The Defense Acquisition 
Guidebook, Vers. 1.0, (10/17/2004), 
paragraph 11.14 still encourages the 
reviews, but the wording has been 
changed to read, “The program man¬ 
ager for an Acquisition Category ID or 
IC program that requires software 
development to achieve the needed 
capability should convene an indepen¬ 
dent expert program review after 
Milestone B and prior to the system 
Critical Design Review. The program 
manager, or other acquisition official 
in the program chain of command up 
to the component acquisition execu¬ 
tive, should also consider independent 
expert program reviews for 
Acquisition Category IA, II, and III 
programs. The independent expert 
review team should report review find¬ 
ings directly to the program manager.” 
This guidebook is available at <http://>. 

2. The CMMI is available for free down¬ 
load at < 

cmmi / models / >. 

3. The PMBOK guide is available for 
purchase at <http://www.pmibook 
Details. aspx?itemID=358&varID=1 >. 

4. The Tri-Service Assessment Office is 
part of the Office of the Under Sec¬ 
retary of Defense (Acquisition, Tech¬ 
nology, and Logistics). The Web site is 
< >, but 
is currently under construction. 

5. Data Item Description (DID) DI- 
IPSC-81427A, Software Develop¬ 
ment Plan, provides a good template 
of the areas that should be included in 
a software project plan. The Institute 
of Electrical and Electronics Engi¬ 
neers Standard 1058-1998, Standard 
for Software Project Management 
Plans, is another source for an example 
project plan. As described in the 
PMBOK guide and the cited DID, a 
project plan is more than a schedule. 

About the Author 

? Timothy K. Perkins 

currently is an indepen¬ 
dent consultant and has 
supported the Software 
Technology Support 

_Center at Hill Air Force 

Base, Utah in providing consulting ser¬ 
vices to the U.S. Air Force, U.S. Depart¬ 
ment of Defense, and other government 
agencies. Perkins has led and participat¬ 
ed in several Independent Expert 
Program Reviews of Acquisition Cate¬ 
gory I acquisition programs as well as 
three Capability Maturity Model® Inte¬ 
gration Acquisition Module pilot assess¬ 
ments. He has been involved in software 
process improvement for the past 17 
years. Before retiring from the Air Force, 
Perkins was Acquisition Professional 
Development Program Level 3-certified 
in Project Management and Systems 
Planning, Research, Development and 
Engineering, and Level 1 in Test and 
Evaluation. Perkins is a Project Man¬ 
agement Institute-certified Project Man¬ 
agement Professional and an authorized 
Theory of Constraints Thinking Process 
instructor. Perkins has a Bachelor of 
Science in Electrical Engineering from 
Brigham Young University and a Master 
of Business Administration from the 
University of Phoenix 


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June 2006 15 

Software Engineering Technology 

Start With “Simple” Earned Value 
On All Your Projects 

Quentin W Fleming and Joel M. Koppelman 

Primavera Systems, Inc. 

When one hears the term Earned Value Management (EVM), there is a tendency to immediately think of the American 
National Standards Institute I Electronic Industries Alliance (ANSI/E1A)-748-1998 required by many government agen¬ 
cies and private contractors. We take exception to this formal definition of EVM. While the ANSI/ELA-748 Standard 
does require full compliance with 32 precise criteria, and will result in EVM, we suggest that a simple form of EVM can 
be gained by implementingjust 10 of these formal criteria on all projects — even software projects. 

I n 1965, the U.S. Air Force acquisition 
managers defined 35 criteria which they 
felt would capture the essence of earned 
value management (EVM), and also satis¬ 
fy their need to oversee the work that was 
being performed for them by private 
industry. Two years later, the Department 
of Defense (DoD) adopted these same 
criteria as their Cost/Schedule Control 
Systems Criteria (C/SCSC). These 35 
standards were then consistently applied 
to all cost type and incentive type con¬ 
tracts for the next three decades. 

Then in 1996, after a rewrite of the 35 
C/SCSC criteria by private industry, the 
DoD accepted the rewriting/rewording of 
these criteria under a new title called the 
Earned Value Management System 
(EVMS). The total number of criteria was 
reduced to 32. Gone were the incompre¬ 
hensible terms of Budgeted Cost for 
Work Schedule, Budgeted Cost for Work 
Performed, and Actual Cost of Work 
Performed, etc. In their place were titles 
like planned value, earned value, and actu¬ 
al costs. People (even busy executives) 
could understand the concept without the 
need for special training or a translator 
being present. 

Private industry in the form of the 
National Defense Industrial Association 
(NDIA) took the defined criteria concept 
one step further. In June 1998, the NDIA 
obtained acceptance of the EVMS in the 
form of the American National Standards 
Institute, termed the ANSI/EIA-748 
Standard. The good news in this story is 
that there has been a consistent applica¬ 
tion of the earned value criteria concept 
applied for more than 40 years. The 
earned value criteria have met the test of 

The bad news is that these criteria 
were originally written for applications to 
complex major system acquisitions. 
Further bad news is that the original 35 
criteria - and the reworded 32 criteria - 

can be overly prescriptive to most of the 
projects in the world, in our opinion. They 
are great for major systems, but likely too 
much for most projects. Somehow a way 
must be found to capture the important 
fundamentals of earned value without 
overly prescribing requirements, which 
often discourages individuals wanting to 
adopt a technique to better manage their 
projects. And, as the ANSI/EIA-748 
Standard becomes more commonplace, 
likely taking the form of a Federal 
Acquisition Requirements clause issued in 
routine procurements, a way must be 
found to scale back the full requirements 
to meet the needs of most projects - even 
small software projects. 

Since 1996, the authors have been 
advocating a simple form of EVM for all 
projects, not just major complex systems 
[1]. Their intent is not to take issue with 
the full application of all 32 criteria when¬ 
ever the project risks and complexities 
warrant full application. However, we 
think all projects could benefit from “A 
methodology for ... objectively measuring project 
perform ance and progress [2] 1 .” EVM, the fun¬ 
damental principles, should be applied to 
any project, of any size, in any industry. 

We have studied the formal criteria 
concept and have summarized just 10 fun¬ 
damental steps which are necessary to 
implement a simple (low-end) form of 
EVM on any project. Perhaps it might be 
called earned value light or earned value 
for the masses. 

There are 10 minimum requirements 
necessary to employ simple earned value. 
This is a good place to start the process. It 
will set the foundation for employing 
EVM, which can be easily expanded to 
satisfy all 32 criteria, should that be 
desired sometime in the future. 

Each of the following 10 fundamental 
steps will also make reference to a specif¬ 
ic EVMS (ANSI/EIA-748 Standard) cri¬ 

Step I: You must define the scope (objec¬ 
tives and deliverables) of the project. 

Satisfying this first criterion is where we 
lose many projects, but it is critical to the 
earned value method. Certain types of pro¬ 
jects, notably software, often give up at this 
point and refuse to go further. Too difficult is 
the cry. Management often relinquishes, 
and the project defaults to simply compar¬ 
ing their cost expenditures - planned costs 
versus actual costs. What a shame. 

On any project, you must define the 
work to be done if for no better reason than 
to know where you are at all times and when 
you are done. To the extent that you can 
based on past experience, you must define 
100 percent of the scope of the project. 
This is true for any project, but it is particu¬ 
larly critical on any project in which you 
intend to measure earned value perfor¬ 

With earned value, we constantly focus 
on the authorized work that has been com¬ 
pleted plus management’s official autho¬ 
rized budget for the completed work. We 
express the status as being 18 percent 
complete, 27 percent complete, 55 percent 
complete, and so forth. Point: If we have 
not defined what constitutes 100 percent 
of the project, how can we ever measure 
our point of percentage completion? We 
can not. 

How does one define a new job when 
specific details are often lacking? There are 
no absolute answers. But one of the most 
useful of all tools available to any project 
manager is the Work Breakdown Structure 
(WBS). The WBS is to the project manager 
what the organization chart is to the execu¬ 
tive. A WBS allows the project manager to 
define a new endeavor by laying out all the 
assumed work within the framework of the 
WBS and then decomposing each element 
into measurable work packages. 

Additionally, once the WBS is assumed 
to constitute a reasonable portrayal of the 
new project, the WBS can then be used to 

I 6 CROSSTALK The Journal of Defense Software Engineering 

June 2006 

Start With “Simple” Earned Value on All Your Projects 

take the next critical steps in the project 
planning process, including make-or-buy 
analysis, risk assessment, scheduling, esti¬ 
mating, and ultimately the authorization of 
budgets to proceed. 

(Reference: EVM Criterion No. 1 [2]: 
Define the authorised work elements for the pro¬ 
gram. A WBS tailored for effective internal man¬ 
agement control is commonly used in this process.) 

Step 2 : You must determine who will per¬ 
form the defined work, including the 
identification of all critical procurements. 

It is important to a project to decide who 
will perform the defined work. 
Experienced workers generally work better 
and faster than inexperienced people, but 
they also cost more. Often, using an expe¬ 
rienced work force is a good investment. 
However, sometimes the project’s own 
organization may have no experience in 
developing a critical new technology, and 
the project must procure the effort from 
another company. These choices are called 
make or buy decisions, and selecting those 
items that must be purchased for the pro¬ 
ject is an essential extension of the scope 
definition process. 

Why is it important to identify the 
work which must be procured outside? 
Because project procurements (versus in- 
house work) create non-forgiving legal 
arrangements. Formal contracts must be 
executed. If you commit to buy something 
that is not what you need, or the require¬ 
ments must be changed, such changes will 
be accommodated, but at a price. Sellers 
love to have changes in scope. Each 
change gives them an opportunity to get 
well from a competitive bid. The earlier the 
procured work is identified and responsi¬ 
bilities assigned, the better such packages 
can be managed by the project. 

By contrast, internal budgets can be 
executed in a more informal way, and the 
fact that everyone is on the same payroll 
allows for some margin of slack. But there 
is no slack with the procured work. 
Procurements must be done properly at 
the start or the project will pay a price. 

Lastly, whether the project work is 
done by the project’s own organization or 
procured from outside the company, the 
measurement and reporting of progress 
must take place. Inside or outside, the pro¬ 
ject must be able to continuously measure 
the earned value versus the actual cost of 
the work being performed. 

(Reference: EVM Criterion No. 2: Identify 
the program organisational structure including 
the major subcontractors responsible for accom¬ 
plishing the authorised work, and define the 
organisational elements in which work will be 
planned and controlled.) 

Step 3: You must plan and schedule the 
defined work. The earned value technique 
could be thought of as representing noth¬ 
ing more than a good scheduling system, 
but with authorized resources (the bud¬ 
gets) embedded into the schedule. The 
schedule reflects the authorized scope and 
timeframe, and the budget is earned for 
work as it is accomplished. 

A formal scheduling system is thus nec¬ 
essary to the employment of earned value 
because it is the vehicle that describes the 
project scope, the planned value, and then 
measures the resulting earned value. The 
project schedule is vital to earned value 
because it reflects the project manager’s 
baseline planned value for everyone to follow. 

On more complex projects, there 
should be some method used to isolate the 
constraints between one task and other 
tasks. What work is holding up other work? 
Typically to satisfy this requirement, some 

“The good news in 
this story is that there 
has been a consistent 
application of the 
earned value criteria 
concept applied for 
more than 40 years. 

The earned value 
criteria have met the 
test of time. ,, 

form of critical path methodology will 
need to be employed. The critical path (and 
near critical paths) on a project must be 
aggressively managed in conjunction with 
negative earned value schedule variances. 

A behind-schedule variance indicates 
that the project is falling behind its base¬ 
line plan. If any late tasks are also on the 
critical path, or they represent high risk 
tasks, they must be aggressively managed 
to successful completion. 

(Reference: EVM Criterion No. 6: Schedule 
the authorised work in a manner which describes 
the sequence of work and identifies the significant 
task interdependencies required to meet the 
requirements of the program.) 

Step 4: You must estimate the required 
resources and formally authorize bud¬ 
gets. Once the project scope has been fully 

defined and subsequently planned and 
scheduled, the next requirement is to esti¬ 
mate the resource requirements (budgets) 
for all defined tasks. Some projects follow 
the start-up sequence of scope, schedule, 
and budget while others follow scope, 
budget, and schedule. Software projects, 
because they are often driven by the avail¬ 
ability of limited resources will schedule 
the project based on available people. 
Either way can be correct as long as scope 
definition comes first. 

Each defined WBS element must have 
a resource value estimated to complete all 
of the specified work, including changes. 
Management will then assess the requested 
resources and approve a value in the form 
of an authorized budget. Individual WBS 
budgets should never contain contingen¬ 
cies or management reserves. Reserves or 
contingencies, if they exist, must be isolat¬ 
ed and owned by the project manager. 

Remember the rule that planned value 
represents two things: the scheduled work, 
plus the authorized budget. Earned value also 
represents two things: the completed autho¬ 
rized work, and the same authorized bud¬ 
get. Thus, in order to plan and then mea¬ 
sure earned value, one needs to schedule all 
defined tasks along with the authorized 
budget necessary to complete the tasks. 

All authorized budgets must be achiev¬ 
able in order to have a viable project baseline. 

( Reference: EVM Criterion No. 9: 
Establish budgets for authorised work with iden¬ 
tification of significant cost elements [labor, mate¬ 
rial, etc.] as needed for internal management and 
for control of subcontractors.) 

Step 5: You must determine the metrics 
to convert planned value into earned 
value. How does one measure the accom¬ 
plishment of planned value into earned 
value? One sets up measurable (verifiable) 
metrics within the baseline schedules to 
quantify the authorized work, and then 
measures the completion of the autho¬ 
rized work. Specific milestones or tasks 
with weighted values are measured as they 
are physically performed. Remember, 
earned value project management is noth¬ 
ing more than managing a project with a 
resource-loaded schedule. 

Since earned value was first introduced, 
various methods have been devised to 
measure project performance. However, 
the most respected methods use some type 
of discrete measurement. Specific mile¬ 
stones representing points in time are 
assigned values, which when fully complet¬ 
ed, the assigned budgeted values are then 
earned. Also, tasks are assigned values, 
which can be measured as they are partial¬ 
ly completed, at which time some value is 

June 2006 17 

Software Engineering Technology 

assigned to the completed work through 
the reporting period. 

(Reference: EVM Criterion No. 7: Identify 
physical products , milestones ; technical performance 
goals\ or other indicators that will he used to measure 

Step 6: You must form a performance 
measurement baseline and determine the 
points of management control referred to 
as Control Account Plans (CAPs). Earned 
value requires use of an integrated project 
baseline. An integrated baseline means that 
the defined work must include both the 
baseline schedule and the authorized bud¬ 
get. Integration takes place within each of 
the specified WBS elements. 

Project management must next specify 
their points of management focus, referred 
to in earned value as CAPs [2]. CAPS are 
placed at selected WBS elements and can 
best be thought of as sub-projects, project 
teams, or subdivisions of the total project. 
The sum of the CAPS will constitute the 
total project baseline. The actual earned 
value performance measurement will take 
place within each of the specified CAPs. 
Total project performance is simply the 
summation of all the detailed CAPs, which 
can be placed at any level of the WBS. 

On some commercial type contracts, the 
total project baseline may sometimes 
include such things as indirect costs and 
even profits or fees to match the total autho¬ 
rized project commitment. The project 
baseline must include whatever executive 
management has authorized the project 
manager to accomplish. 

Most likely, internal company projects 
typically do not contain indirect costs or 
profits. Many (perhaps most) internal pro¬ 
ject baselines will simply represent the sum 
of the defined CAPs, which are often made 
up from direct labor hours only. The autho¬ 
rized project baseline constitutes whatever 
management has decided it should be. 

Note: The referenced EVM criterion 
No. 8 contains a lot of words, most of 
which are beyond the requirement for sim¬ 
ple earned value applications. 

(Reference: EVM Criterion No. 8: Establish 
and maintain a timephased budget baseline, at the 
control account level, against which program perfor- 
mance can be measured. Initial budgets established 
for performance measurement will be based upon 
either internal management goals or the external cus¬ 
tomer-negotiated target cost including estimates for 
authorised but undefined work. Budget for far-term 
efforts may be held in higher-level accounts until an 
appropriate time for allocation at the control account 
level. On government contracts, if an over-target 
baseline is usedfor performance measurement report¬ 
ing purposes, prior notification must be provided to 
the customer.) 

Step 7: You must record all direct costs by 
project consistently with the authorized 
baseline budgets, in accordance with the 
organization's general books of accounts. 

This criterion simply requires that project 
managers be informed as to how much 
money they have spent on their projects - a 
simple requirement that some organizations 
find extremely challenging. The reason is 
many organizations have been functionally 
oriented for so long that they have lost their 
ability to focus on individual project perfor¬ 
mance. It is absolutely essential that direct 
costs be identified by project as work pro¬ 

In order to employ earned value on any 
project, the actual costs must be aligned to 
the authorized project budgets. Remember 
the rule that planned value represents the 
authorized work plus budget, which is then 
converted into completed work and the 
same budget to represent the earned value. 
Earned value must then be relatable to the 

.. whether the project 
work is done by the 
project's own 
organization or procured 
from outside of the 
company the 
measurement and 
reporting of progress 
must take place.” 

actual costs in order to determine the cost 
efficiency factor, called the Cost 
Performance Index (CPI). The CPI is likely 
the single most important metric for any 
project employing earned value. 

There is a trend in projects employing 
earned value to measure performance on a 
weekly basis. We need to understand what 
this means, and what it does not mean. 
Weekly EVM means the measurement of 
internal direct labor hours. On a weekly 
basis, the company labor tapes will produce 
a planned value, earned value, and actual 
hours for internal direct labor hours only. 
Direct labor dollars, indirect costs, pur¬ 
chased articles, travel, etc., are generally not 
available on a weekly basis. Weekly perfor¬ 
mance measurement takes place on the 
internal direct labor hours only. 

(Reference: EVM Criterion No. 16: Record 
direct costs in a manner consistent with the budgets 

in a formal system controlled by the general books of 

Step 8: You must continuously monitor the 
earned value performance to determine 
cost and schedule departures from the 
baseline plan: both schedule variances 
(earned value less the planned value) and 
cost variances (earned value less the actu¬ 
al costs). Projects employing earned value 
must monitor their cost and schedule results 
against the authorized baseline for the dura¬ 
tion of the project. Management will focus 
their primary attention on exceptions to the 
baseline plan, particularly those that are 
greater than previously defined acceptable 
tolerances. Earned value is thus a management 
by exception concept. 

A negative earned value schedule vari¬ 
ance simply means that the value of the 
work performed does not match the value 
of the work scheduled, that is, the project is 
falling behind in its scheduled work plan. 
Each behind-schedule task should be 
assessed as to its criticality. If the late tasks 
are on the critical path, or if the tasks carry 
a high risk to the project, then efforts must 
be taken to get the late tasks back on sched¬ 
ule. However, additional project resources 
should not typically be spent on low-risk 
tasks or tasks that have positive critical path 

The single most important aspect of 
employing earned value is the cost efficien¬ 
cy readings it provides. The difference 
between the value of work earned, versus 
the costs incurred to accomplish the work 
provides the cost efficiency factor. If the 
project spends more money than it receives 
in value, this reflects an overrun condition. 
Overruns are typically non-recoverable. 
Overruns expressed as a percentage value 
have been found to deteriorate unless the 
project takes aggressive actions to mitigate 
the condition. 

Perhaps of greatest benefit, the earned 
value cost efficiency rate has been found to 
stabilize from the 20 percent point of a pro¬ 
ject completion. The cost efficiency factor, 
CPI, is thus an important metric for any 
project manager or portfolio executive to 

(Reference: EVM Criterion No. 22: At least 
on a monthly basis, generate the following informa¬ 
tion at the control account and other levels as neces¬ 
sary for management control using actual cost data 
from, or reconcilable with, the accounting system: 

1. Comparison of the amount of planned budget 
and the amount of budget earned for work 
accomplished. This comparison provides the 
schedule variance. 

2. Comparison of the amount of the budget earned 
and the actual (applied where appropriate) 
direct costs for the same work. This comparison 

I 8 CROSSTALK The Journal of Defense Software Engineering 

June 2006 

Start With “Simple” Earned Value on All Your Projects 

provides the cost variance.) 

Step 9: Using earned value data, you must 
forecast the final required costs based on 
actual performance and keep manage¬ 
ment apprised so they can take corrective 
actions if necessary. One of the more ben¬ 
eficial aspects of earned value is that it pro¬ 
vides the capability to quickly and indepen- 
dendy forecast the total funds required to 
complete a project, commonly referred to as 
the estimate at completion. Based on actual 
cost and schedule performance against the 
baseline plan, a project is able to accurately 
estimate the total funds it will require to fin¬ 
ish the job within a finite range of values. 

Often, management or customers will 
have a preconceived notion of what final 
costs should be or what they would like 
them to be. If the earned value statistical 
forecast of estimated final costs are greater 
than the official project manager’s estimate to 
complete the project, someone needs to rec¬ 
oncile these professional differences of 

Actual performance results on any pro¬ 
ject, good or bad, are in effect sunk costs. 
Such costs represent what the project has 
actually achieved in performance. Thus any 
improvements in performance must come 
from the future work - tasks that lie ahead 
of the project’s status date. Earned value 
allows the project manager to accurately 
quantify the cost and schedule performance 
achieved to date. And if the results achieved 
to date are less than that desired by manage¬ 
ment, the project can exert a more aggres¬ 
sive posture to influence the future work. 

Earned value allows the project to accu¬ 
rately quantify the value of its work it has 
achieved. It also allows the project to quan¬ 
tify the value of the future work in order to 
stay within the objectives set for the project 
by management. Likely, the single most 
respected method to forecast the final cost 
results is to assume that the project will con¬ 
tinue at its established cost efficiency rate, 
CPI; it will get better or worse within a nar¬ 
row, finite range. 

(Reference: EVM Criterion No. 27: Develop 
revised estimates of cost at completion based on per¬ 
formance to date, commitment values for material, 
and estimates of future conditions. Compare this 
information with the performance measurement 
baseline to identify variances at completion important 
to company management and any applicable cus¬ 
tomer reporting requirements including statements of 
funding requirements.) 

Step 10: You must manage the authorized 
scope by approving or rejecting all 
changes, and incorporating approved 
changes into the project baseline in a time¬ 
ly manner. The project performance mea¬ 

surement baseline, initially put into place at 
the project start, is only as good as the man¬ 
agement of all proposed new changes to the 
baseline for the duration of the project. 
Performance baselines quickly become 
invalid simply by failing to incorporate 
changes into the approved baseline with the 
addition or deletion of added work scope. 

All new change requests of the project 
must be quickly addressed, either by approv¬ 
ing such changes or by rejecting them. All 
project managers should have sufficient 
authority to say no. 

In order for the initial baseline to remain 
valid, every change must be controlled. 
Maintaining an approved baseline can be as 
challenging as the initial definition of the 
project scope at the start of the project. 

(Reference: EVM Criterion No. 28: 
Incorporate authorised changes in a timely manner, 
recording the effects of such changes in budgets and 
schedules. In the directed effortprior to negotiation of 
a change, base such revisions on the amount estimat¬ 
ed and budgeted to the program organisations.) 


Earned value project management is not a 
difficult concept to understand or employ. 
It is certainly not as complicated a process 
as some have made it to be throughout the 
years. We have concluded that effective 
earned value can be achieved by simply 
applying these 10 steps and can be applied 
to any project, of any size, in any industry. 
Earned value is for the masses. 

Again, we are not taking issue with the 
EVMS or ANSI-EIA-748 Standard. What 
we are suggesting is starting with the 

implementation of earned value in a limit¬ 
ed way, on all projects, by simply taking the 
10 simple steps as outlined here. 

As you read over these 10 steps, we hope 
you come to the conclusion that employing 
earned value project management consists 
of nothing more than simply following fun¬ 
damental best project management process¬ 
es. As was stated nicely by a gentleman from 
the United Kingdom: 

Whilst you can practice good project 
management without EVM, you can¬ 
not practice EVM effectively without 
good project management. [3] 

We could not have stated it better.♦ 


1. The numbers as shown relate to the 
sequence of the listed criteria in both the 
EVMS and ANSI-EIA-748 Standard. 


1. Fleming, Quentin W. and Joel M. 
Koppelman. Earned Value Project 
Management . 3rd ed. Newtown 
Square, PA: Project Management 
Institute, 2005. 

2. Project Management Institute. A 
Guide to the Project Management 
Body of Knowledge . 3rd ed. Newtown 
Square, PA: Project Management 
Institute, 2004. 

3. Crowther, Steve. “Best of British: 
Earned Value Management.” British 
Association for Project Management 
June 1999:13. 

About the Authors 

Quentin W. Fleming is 

a management consul¬ 
tant specializing in 
earned value. He has 
been a consultant to the 
senior staff at Primavera 
Systems, Inc. since 1993. He, along with 
Joel M. Koppelman, is co-author of 
“Earned Value Project Management,” 
published initially in 1996 by the Project 
Management Institute. The third edition 
of this book was released in 2005. His 
personal Web site is <www.quentinf. 

14001 Howland WY 
Tustin, CA 92780 
Phone: (714) 731-0304 
Fax: (714) 731-0304 

Joel M. Koppelman is 

the co-founder and Chief 
Executive Officer (CEO) 
of Primavera Systems, 
Inc. He, along with 
Quentin W Fleming, is 
co-author of “Earned Value Project 
Management,” published initially in 1996 
by the Project Management Institute. 
The third edition of this book was 
released in 2005. His corporate Web site 
is <>. 

Primavera Systems, Inc. 

Three Bala Plaza West 
Bala Cynwyd, PA 19004 
Phone: (610) 667-8600 

June 2006 19 

Statistical Methods Applied to EVM: The Next Frontier 

Walt Lipke 

Retired Software Manager 

An objective of Earned Value Management is to provide a means forpredicting the outcome of a project. Inherently, the out¬ 
come is largely determined in the planning, and completion forecasting commonly occurs with analysis of project performance. 

Having the project plan, management would like to be able to quantify its risk: What is the likelihoodfor having a success¬ 
ful project with this plan ? How much should be allocated to reserves to achieve a high probability of success? If reserves are 
constrained to maintain a bid price in the competitive range, what is the probability of having a successful outcome? During 
project execution, management desires to answer this question: Can we state with confidence when the project can be expected 
to complete and simultaneously describe its projected final cost? The application of statistical methods facilitates answering 
these questions. This article describes the elements necessary for performing statistical analysis. 

T he worth of Earned Value 
Management (EVM) has been 
demonstrated over the 35 plus years of 
application to many projects. There is 
substantive evidence of its positive influ¬ 
ence on project outcome results. EVM 
fosters several good management prac¬ 
tices that contribute to successful project 
performance: organization, accountabili¬ 
ty, planning, risk assessment, tracking, 
reporting, controlling, etc. Overarching 
these elements, in my opinion, is the 
most significant contribution to the 
improvement of the state of manage¬ 
ment practice is that EVM has brought 
science to the management of projects. 
Without numbers, scientific management 
is not possible. Because of EVM, project 
managers have numbers with a sound 
basis. Performance of a project has a 
quantitative description with meaning. 
And, in turn, the numerical description 
provides project managers with informa¬ 
tion useful for guiding and controlling 
the project. From a relatively simple con¬ 
cept, a quantum leap has been made for 
the management of projects — Earned 
Value Management. 

Several formulas derived from EVM 
measures are available for predicting the 
final cost of projects. These cost predic¬ 
tion formulas have been well studied 
over the last 15 years. From the research, 
the EVM community has an under¬ 
standing of project behaviors. We now 
know how to calculate the most opti¬ 
mistic and predicted outcome for cost. 
And, we understand that projects per¬ 
form less efficiently as they progress 
toward completion. For very large pro¬ 
jects, we know from early results the 
range of likely final cost outcomes. 
Significant strides have been made in 
project management from the use of 
EVM measures; project managers now 
have available a few research-derived 
prediction tools. 

Is There a Path to Improved 

In truth, advancement of outcome predic¬ 
tion knowledge for EVM-based projects 
has remained stagnant for nearly a decade. 
The prediction findings cited previously 
were established several years ago and 
have not been improved upon. Although 
there is more than 35 years of numerical 
evidence of project performance for 
many types of applications (defense, con¬ 
struction, software, etc.) from several 
countries, this EVM data is not available 
for research. If we could only get by the 
unfounded worry that by divulging our 
data for completed projects we are some¬ 
how giving up sensitive information that 
could somehow negatively impact our 
company. Possibly, the influence of the 
Sarbanes-Oxley Act [1] may help to over¬ 
come this roadblock to advancement of 
EVM. Let us hope so. The sharing of data 
will not only lead to improved prediction 
methods, it will also promote continuing 
improvements to EVM itself. 

In the previous discussion, I have 
established that a researcher, desiring to 
test a theory concerning EVM, has only 
limited data — specifically, his own. Thus, 
the question becomes: “What advance¬ 
ment can be made knowing the 
researcher’s hypothesis cannot be fully 
tested and validated because of the inac¬ 
cessibility of broad-based data?” At this 
time, many of you will probably say, “Not 
much.” Even with today’s situation, we 
can improve our capability to predict out¬ 
comes. Here is my answer to the question: 
Apply well-established statistical methods. 
Statistical methods are proven calculation 
techniques by which one can infer project 
outcomes with confidence. Using these 
methods, past performance can provide a 
vision of the future. 

Is It Difficult to Do? 

Good question. Without a background in 

statistics, it may be somewhat overwhelm¬ 
ing in the beginning. However, with a small 
amount of training in the applicable areas 
and some practice with EVM data, profi¬ 
ciency will come. In the absence of statis¬ 
tical tools applicable to EVM, you will 
need to develop spreadsheets until the 
commercial EVM tool sources catch up to 
the market. Creating the spreadsheets will 
not be difficult for someone adept at it and 
can likely be accomplished in semi-profes¬ 
sional form within a short amount of time 
(my estimate is two to three weeks). 

Our Focus 

Before we lose ourselves in the discussion 
of statistics, the focus of this article needs 
to be stated. The objective is to provide 
project managers the ability to answer the 
following questions: 

• What is the likelihood for having a 
successful project with this plan? 

• How much should be allocated in 
reserves to achieve a high probability 
of success? 

• If reserves are constrained to main¬ 
tain the bid price in the competitive 
range, what is the probability of hav¬ 
ing a successful outcome? 

• Can we state with confidence when 
the project can be expected to com¬ 
plete and simultaneously describe its 
projected final cost? 

Certainly, with the ability to answer 
these questions, project managers and 
their superiors can make better informed 
decisions. By taking the correct manage¬ 
ment action at the right time, we can 
expect improvement in the success rate 
for projects and the avoidance of failure. 

Applying Statistics to EVM 

To apply statistical methods, a few prop¬ 
erties of data are needed before we can 
address these questions. First, we need to 
establish that the data can be described 
by Normal distribution. If it can, then 

20 CROSSTALK The Journal of Defense Software Engineering 

June 2006 

Statistical Methods Applied to EVM: The Next Frontier 

our ability to draw inferences and make 
predictions is greatly simplified. The sec¬ 
ond property is the value representing 
the mean or average value of the obser¬ 
vations. The third property is the varia¬ 
tion in the observed data values. These 
properties are interconnected; without 
the characterization of the data (i.e., its 
type of distribution), neither the mean 
nor the variation can be determined cor¬ 
rectly. And without the mean and varia¬ 
tion, the focus questions cannot be 

Let us assume the observances of the 
EVM indicator are normally distributed; 
Figure 1 is an example of Normal distrib¬ 
ution. When this is the case, the distribu¬ 
tion is symmetrical around its peak, the 
most frequently observed value. The 
mean of the distribution is the value asso¬ 
ciated with the peak. The width or spread 
of the distribution is a function of the 
variation in the observed values — the larg¬ 
er the spread, the greater the variation 1 . 

From this information, inferences or 
predictions can be made. For example, we 
can calculate at a specified precision the 
range of values for the EVM indicator 
which encompasses its true value or pre¬ 
dicted outcome value. In statistical terminolo¬ 
gy, the end values of this range are confi¬ 
dence limits (CL). These limits are generally 
calculated at 90 or 95 percent precision 
and are commonly termed xx percent confi¬ 
dence level. For example, the CL calculated 
using the 95 percent CL provide a range 
of values in which we have 95 percent 
confidence of including the true value of 
the mean. To make this clearer, I will 
express it mathematically [2]: 

CL = Mean ± Z * o/Vn 


Z is a value representing the 90 or 95 
percent confidence level 
a is a number representing the 
variation in the observed values 
n is the number of observances 

This equation is not very daunting, and 
possibly you are beginning to see the use¬ 
fulness of calculating CL. Clarity with 
regard to its application should be realized 
from the coming examples. 

Another fundamental needed for 
having the ability to answer our ques¬ 
tions is the calculation of the probability 
for achieving a specified result. In 
essence, the calculation obeys the above 
equation. However, instead of calculat¬ 
ing confidence limits, we compute the 
value of Z [2]: 

Figure 1: Normal Distribution 

Z = (X - Mean) - (o/Vn) 


X is a value for which an associated 
probability is desired 

From the calculated value of Z, the 
probability that the true value of the mean 
is less than or equal to the value X can be 
obtained from a mathematical table of 
Normal distribution [2], or by using a 
spreadsheet function to perform the con¬ 
version. For example, the statistical func¬ 
tion NORMSINV from Microsoft Excel 
may be used to perform the calculation. 

Although it may not be totally clear at 
this point, with these two fairly simple 
equations, every one of the earlier ques¬ 
tions can be answered. 

Calculation Examples 

For understanding, let us perform a few 
calculations pertinent to our objectives. 
We will continue with the assumption 
that the periodic observations of the 
Cost Performance Index (CPI) are nor¬ 
mally distributed. For the example, the 
cost performance efficiency (cumulative 
CPI) of a software project is found to be 
equal to 0.931. The cumulative value of 
CPI is taken to be a good estimate of 
the mean of the observations. The vari¬ 
ation of the periodic values of CPI, i.e., 
the estimate of the standard deviation 
(G), is equal to 0.340. The number of 
periodic observations is 16. The level of 
confidence desired is 90 percent; from a 
normal distribution table, the value of Z 
is determined to be 1.645. From this 
information, we can calculate the confi¬ 

dence limits: 

CL = Mean ± Z * a/Vn 
= 0.931 ± (1.645) * (0.340 / Vi 6) 

= 0.931 ±0.140 
= 1.071,0.791 

The values calculated for the confidence 
limits, 0.791 and 1.071, identify the range 
for the mean of CPI. Furthermore, we have 
90 percent confidence that the true value of 
the mean of CPI is within these limits. 

With this information, we can predict 
the high and low values of the final cost 
with 90 percent confidence using the fol¬ 
lowing formula: 



IE AC (Independent Estimate at 

Completion) is the forecast cost 
at project completion 
BAC (Budget at Completion) is the 
planned cost for the project 

Assuming BAC = $1,000, the range for 
final cost is $1,264 and $934. Now, assume 
that in order to not consume all the man¬ 
agement reserve, the cost performance 
efficiency must be greater than or equal to 
0.850. Another way of viewing this is the 
reciprocal of CPI (mean), 1/0.931 = 
1.074, must be less than or equal to the 
reciprocal of 0.850, or 1.176. With these 
numbers and the parameter values provid¬ 
ed in the previous example, the probabili¬ 
ty of a having a successful project can be 

June 2006 21 

Software Engineering Technology 

Z = (X - Mean) ■=■ (<Wn) 

= (1.176 - 1.074) -r (0.340A/16) 

= 0.102-0.085 

= 1.200 

Converting Z (using Normal distribution), 
we obtain the probability of the project’s 
final cost being less than its allocated bud¬ 
get to be 88.5 percent. 

Is It Really That Simple? 

No. I wish it was. The previous descrip¬ 
tion of the calculations illustrates the idea 
in its simplest form, but there are six ele¬ 
ments which add complexity: 

• Normality. 

• Finite population. 

• Equal samples. 

• Anomalous behavior. 

• Fewer than 30 observations. 

• Increasing inefficiency. 

Recall in the previous discussion and 
the calculation examples, it was assumed 
that the periodic values of CPI are nor¬ 
mally distributed. This is not the case; 
the distribution is right-skewed. From 
previous work, I have shown that by 
applying logarithms, the distributions of 
CPI and Schedule Performance Index 
SPI(t) 2 can be made to appear normal [3]. 
Figure 2 illustrates the transformation of 
a right-skewed distribution to its sym¬ 
metrical normal distribution by the 
application of logarithms. 

The second element, finite population, 
is extremely significant. Statistical meth¬ 
ods assume the population under exami¬ 
nation is infinite. However, projects are 
finite — they have a start and an end. For 
finite populations, the statistical calcula¬ 
tions must be adjusted. As the project 
moves toward completion, the adjustment 
causes the probability of success to move 

toward 100 percent or zero; i.e., the pro¬ 
ject completed successfully or it did not. 
Likewise, the finite population adjustment 
causes the upper and lower confidence 
limits to approach each other, concluding 
at the same value, the mean. 

Statistics assumes that each obser¬ 
vance is of equal size. For example, if we 
are trying to infer the proportion of black 
marbles to white ones in a huge barrel, we 
might choose to draw independently 10 
samples of 10 marbles. It would not be 
correct statistical practice to draw 10 sam¬ 
ples of varying size. In our situation, each 
observance of CPI represents differing 
amounts of actual cost. To perform the 
statistical analysis in the appropriate man¬ 
ner, periodic CPIs must be developed for 
equal cost samples [4]. From the project 
data examined to date, the estimate of the 
variation is slightly smaller for equal cost 
samples than its value calculated from 
simply using the reported periodic CPI 

Certainly, if there is one periodic value 
that is much different from the remainder, 
we have to question whether or not to 
include it in our calculations. By including 
the anomaly, we might predict a project 
outcome much differently from the pre¬ 
diction made excluding it. The inclusion 
of the anomaly has the potential of caus¬ 
ing an incorrect management action as 
well. My recommendation is to identify 
anomalies using the methods of Statistical 
Process Control, applying the Shewhart 
rule only [5]. Removing anomalous behav¬ 
ior improves project outcome prediction 
and its identification enables appropriate 
management action. 

When the number of observances is 
fewer than 30, it is accepted practice to 
perform the statistical calculations using 

the Student—t distribution 3 . When the 
number is 30 or greater, Normal distribu¬ 
tion is used. 

Lastly, from research of CPI behavior, 
it is known that cost performance effi¬ 
ciency tends to be worse at project com¬ 
pletion than it is earlier in the project [6]. 
Although a similar study of schedule per¬ 
formance behavior has not been made, it 
is conjectured that SPI(t) behaves analo¬ 
gously to the findings for CPI [7]. Thus, 
from this tendency to worsen, the fore¬ 
cast final CPI and SPI(t) will generally be 
less than its respective present value. To 
account for this behavior, compensation 
is applied at each of the periods to fore¬ 
cast the final values [7]. The compensa¬ 
tion affects the variation calculated; the 
variation of the compensated periodic 
values of CPI, or SPI(t), is likely to be 
somewhat less than for the uncompensat¬ 
ed values. 

Hopefully these complexities are not 
an overwhelming deterrent. Obviously, 
they do add to the calculation burden. 
However, with some ingenuity all can be 
handled without much trouble through 
the use of spreadsheets, dealing with the 
complexity is really not that difficult. Keep 
in mind the benefit to your project man¬ 
agement having reliable outcome predic¬ 
tion. The value of good prediction far 
outweighs the discomfort of accommo¬ 
dating the complicating elements dis¬ 

Calculation Examples - 
Including Complexity 

Let us perform the calculations again and 
account for the elements adding complex¬ 
ity. For these calculations, assume that 
none of the observations exhibit anom¬ 
alous behavior and the distribution is log¬ 
normal. Also assume the compensated 
CPI mean is 0.911 and that the variation 
of the compensated monthly values is 
0.250 for the Normal distribution. Note 
that both values are somewhat less than 
those used in the earlier example, just as 
we would expect. Recall from earlier dis¬ 
cussion that the final cumulative CPI 
tends to be less than the present value, and 
the variation is smaller from the effects of 
equal samples and applying compensation. 
For this example, the total population of 
observances for the project is 21, and 
from the previous example the number of 
observations (n) is equal to 16. 

For the confidence limits, the follow¬ 
ing calculation is made: 

In CL = In Mean ± Z * o/Vn * Adjustment 
for finite population 4 

22 CROSSTALK The Journal of Defense Software Engineering 

June 2006 

Statistical Methods Applied to EVM ... the Next Frontier 

= In (0.911) ± (1.645) * (0.250A16) * 

V ((21 - 16)/(21 -1)) 

= -0.093 ± 1.645 * 0.062 * 0.5 
= -0.093 ± 0.051 
= -0.042, - 0.144 
CL = 0.959, 0.866 

Using the confidence limits, the final cost 
prediction is calculated: IEAC = $1,155, 

The probability of having a successful 
project is computed as follows: 

Z = (In X - In Mean) ■=■ 

[(cr/Vn) * Adjustment for finite 

= (In 1.176 - In 1.074) + [(0.250A/16) * 
V((21 - 16) / (21 - 1))] 

= (0.163 - 0.093) 4- [(0.250A16) * 

V((21 - 16)/(21 - 1))] 

= 0.070 - [0.062 * 0.5] 

= 2.240 

Converting Z, using the Student-t distrib¬ 
ution, the probability of having a suc¬ 
cessful project outcome is determined to 
be 98.0 percent. 

The differences between these esti¬ 
mates and those computed previously are 
very noticeable. The range of the confi¬ 
dence limits is very much smaller for the 
more complex calculation ($112 versus 
$330), thereby causing the final high and 
low cost estimates to be much closer. The 
probability of having a successful project 
is increased by nearly 10 percent for the 
second calculation (even when using the 
Student-t distribution). In other words, 
by accounting for the complexities, the 
project manager has a much more refined 
estimate of the final outcome. 


From the past studies performed on 
EVM measures from large defense con¬ 
tracts, managers and analysts have some 
ability to forecast the final cost of pro¬ 
jects. The ability to advance forecasting 
beyond its present status, that is to pro¬ 
jects which are neither defense related 
nor large (as are many software or infor¬ 
mation technology projects), is hampered 
by the lack of accessible broad-based 
data for research. Consequently, re¬ 
searchers have little facility to test their 

To circumvent the lack of data for 
experimentation, the application of sta¬ 
tistics is proposed. The use of statistical 
methods for inferring outcomes is a long¬ 
standing mathematical approach. The 
methods applied to EVM measures are 
shown to be relatively simple in concept. 
However, several elements are discussed, 

which cause the application to have 
added complexity. Including the com¬ 
plexity elements in the method is shown 
to provide managers with a more refined 
forecast of project outcome. 

Final Remarks 

My desire for this article is that it will 
promote interest in the application of 
statistical methods to EVM measures. If 
interest is generated, it is my belief that 
other positive behaviors may follow: 

• Project data records will become 
more meticulous and, thus, become 
more useful for further research. 

• Data sharing will occur leading to a 
common EVM data repository for 

• As the use of statistical methods 
propagates, automated tools will 
emerge, in turn further expanding the 

• If this vision of the next frontier 
becomes reality, project management 
will make another quantum leap for¬ 
ward. ♦ 


1. The statistical variation of observed 
measures is expressed as standard 
deviations. See [2] (or any text on sta¬ 
tistics) for a complete description. 

2. SPI(t) is the Schedule Performance 
Index (time based) and is a measure 
of schedule performance efficiency 

3. The Student-t distribution approach¬ 
es the Normal distribution as the 
number of observations becomes 
large (>30) [9]. 

4. The adjustment for a finite popula¬ 
tion is v [(N—n) / (N—1)], where n is 
the number of observations made 
thus far and N is the total population 
when the project is complete; that is, 
the number of observations expected 
to be made. 


1. The Sarbanes-Qxlev Act of 2002 . Pub. 
L. H.R.3763. 7 Jul. 2002. <http://news. docs/ 

2. Crow, E.L., F.A. Davis, and M.W. 
Maxfield. Statistics Manual . New York: 
Dover, 1960. 

3. Lipke, W “A Study of the Normality of 
Earned Value Management Indicators.” 
The Measurable News Dec. 2002: 1-16. 

4. Lipke, W “Achieving Normality for 
Cost.” The Measurable News 
Fall/Winter 2003: 1-11. 

5. Pitt, H. SPC for the Rest of Us . 
Reading, MA: Addison-Wesley, 1995. 

6. Christensen, D.S., and S.R. Heise. “Cost 
Performance Index Stability.” National 
Contract Management Journal 25 
(1993): 7-15. 

7. Lipke. W “Connecting Earned Value to 
the Schedule.” CROSSTALK June 2005 

8. Lipke, W “Schedule Is Different.” The 
Measurable News Summer 2003: 31-34. 

9. Wagner, S.F. Introduction to Statistics . 
New York: Harper Collins, 1992. 

About the Author 

y Walt Lipke recently 

i H retired as the deputy chief 

I 9 of the Software Division 

1 at the Oklahoma City Air 

mately 600 people, primarily electronics 
engineers. He has more than 35 years of 
experience in the development, mainte¬ 
nance, and management of software for 
automated testing of avionics. In 1993, 
with his guidance, the Test Program Set 
and Industrial Automation (TPS and IA) 
functions of the division became the first 
U.S. Air Force activity to achieve Level 2 of 
the Software Engineering Institute’s 
Capability Maturity Model® (CMM®). In 
1996, these functions became the first 
software activity in federal service to 
achieve CMM Level 4 distinction. Under 
Lipke’s direction, the TPS and IA func¬ 
tions became ISO 9001/TickIT registered 
in 1998. These same functions were hon¬ 
ored in 1999 with the Institute of 
Electrical and Electronics Engineers’ 
Computer Society Award for Software 
Process Achievement. Lipke has published 
several articles and presented at confer¬ 
ences on the benefits of software process 
improvement and the application of 
earned value management and statistical 
methods to software projects. He is the 
creator of the technique Earned 
Schedule®, which extracts schedule infor¬ 
mation from earned value data. Lipke is a 
professional engineer with a master’s 
degree in physics. 

1601 Pembroke DR 
Norman, OK 73072 
Phone: (405) 364-1594 

® 2003 by Walt Lipke. All Rights Reserved. 

June 2006 23 

Defining Short and Usable Processes® 

Timothy G. Olson 

Quality Improvement Consultants , Inc. 

Many processes and procedures are large or difficult to use. The situation becomes even worse when complexity is involved. 
Putting large or difficult-to-use documentation on a Web site does not usually solve the problem. This article describes best 
practices for defining short and usable processes and procedures. These best practices have been used at real organisations over 
the last few years to define short and usable processes and procedures. Measurable results include cutting organisational 
processes and procedures in half while making them more usable (eg., reducing 600 pages to 300 pages). The objectives of 
this article are to describe common problems with process documentation, including some human aspects of using process doc¬ 
uments, discuss some best practices for defining short and usable processes and procedures, describe some success stories in real 
organisations, and provide some lessons learned. 

B efore we get into describing industry 
best practices, it is good to under¬ 
stand common problems with process 
documentation. Table 1 lists a summary of 
common problems. 

How do we address the common 

problem with process documentation? 
One place to start is to recognize that not 
all documentation is used the same way. In 
this article, the term process documentation is 
used interchangeably with policies, standards, 
processes, and procedures. Table 2 contains a 

list of the types of process documentation 
and the ways in which these are used. 

Figure 1 [2] identifies the types of 
process documents and some critical rela¬ 
tionships among those documents. 

Process Documentation 
Usage Modes 

Processes and procedures have different 
levels of users [3]. Some users have never 
used the process (i.e., beginner users). 
Some users have used the process a few 
times, but need guidance and lessons 
learned (i.e., intermediate users). Some 
users have used the process many times 
and may even be responsible for running 
the process (i.e., experts). The next sections 
will describe the three levels of documen¬ 
tation: expert, intermediate, and beginner. 

Expert Mode Documentation 

Expert mode documentation is short and 
concise [3]. When a pilot flies an airplane, 
he or she does not pull out a training man¬ 
ual; they use expert checklists for takeoff 
and landing. Expert mode documentation 
is made for experts and does not contain 
any training material. See Figure 2 (page 
26) for an example of expert mode. 

Most people want expert mode docu¬ 
mentation because it is short. The prob¬ 
lem with expert mode documentation is 
that not everybody is an expert. For exam¬ 
ple, not everyone can read a checklist for a 
rocket scientist (sometimes you really need 
to be a rocket scientist). Putting expert 
mode documentation in the hands of 
non-experts can be dangerous. 

Why do experts need documentation if 
they are experts? Because people can for¬ 
get things. This is why checklists are so 
powerful. Experts can also leave your 
organization, taking precious organiza¬ 
tional knowledge with them. This is why 
expert knowledge should be documented. 

© 2006 Process Assets, LLC. All Rights Reserved. 

Table 1: Common Problems Process Documentation 



1. Too Big 

Most process documentation is too big. Blaise Pascal 
once said, "1 have made this letter longer than usual 
because 1 lack the time to make it shorter." This quote 
applies to most processes and procedures. Process 
documentation should be short, concise, and usable. 

2. Not Enough 

Most processes and procedures lack pictures and 
diagrams. If a picture is worth a thousand words, process 
documentation should have more pictures. Good process 
documentation should be a mixture of pictures and words. 
The best pictures are well-thought-out diagrams. The best 
diagrams for process documentation are process models. 

3. Poorly Designed 

Processes and procedures usually violate documentation 
design and good writing principles. Principles such as 
chunking 1 (i.e., seven plus or minus two), consistency, 
etc., are usually not used [1]. 

4. Unusable and 

One Size Fits All 

Most processes and procedures are not designed with 
customers and users in mind, making them hard to use. 

Much documentation also has the one-size-fits-all 
mentality because it does not consider expert, 
intermediate, and beginner users. 

5. Mixed Information 

Policies, standards, processes, procedures, and training 
are all different types of information [2]. Most process 
documentation mixes these different types of information 
into the same paragraphs as if they were all used the 
same way. Each one of these document types has a 
different usage scenario. 

6. Written 

Process documentation is not a novel, and is not meant 
to be read linearly (i.e. from beginning to end). Process 
documentation is reference material that is meant to be 
used non-linearly. This is why labeling is critical so that 
users can find information quickly [1]. 

7. Hard-to-Find 
Information Fast 

Users of documentation will look for information for a few 
minutes. But if they cannot find the information quickly, 
many times they will give up in frustration and not use the 
processes or procedures. This can lead to serious 
nonconformance problems for many organizations. 

8. Shelfware 

Most process documentation becomes shelfware (i.e. 
collects dust on a shelf). Online processes (e.g. on an 
intranet) must be well designed or they will also 
become unused Web-ware. 

24 CROSSTALK The Journal of Defense Software Engineering 

June 2006 

Defining Short and Usable Processes 

Document Type 



• Used by senior management to set direction in an 

• States principles that organizations should follow. 


• Specifies the sections of a document, and provides a 
description of what goes into those sections. 

• Makes the content of documents repeatable. 


• What happens over time to produce a desired result(s). 

• Should answer the five Ws: who, what, where, when, 
and why. 


• How-to or step-by-step information [1]. Example 
procedures are checklists, forms, and step/action tables. 

• Implements part of a process. 

Table 2: Types of Process Documentation and Their Uses 

Intermediate Mode Documentation 

Intermediate mode documentation uses 
the expert mode documentation, but 
builds and adds to it by providing guid¬ 
ance and lessons learned. For example, 
guidance is very useful to people who do 
not have to follow a process or procedure 
very often. Even experts forget guidance 
and lessons learned for an annual process 
or an infrequently used process. Having 
guidance available to those who need or 
want it is very useful. Both Figure 2 and 
Table 3 (page 26) together are examples of 
intermediate mode. 

Typically, guidance and lessons learned 
are not auditable. Process phases and pro¬ 
cedure steps are required and auditable, but 
the supporting guidance and lessons 
learned are there for support only. One best 
practice is to distinguish between required 
steps and optional guidance. Some organi¬ 
zations have chosen to label guidance and 
lessons learned with a guidance label. 

Beginner Mode Documentation 

Beginner mode documentation uses the 
intermediate mode documentation, but 
adds training to it. Beginners should feel 
free to use the training manuals until they 
become familiar with the process. 
Beginners should also be mentored as 
appropriate. Some processes are simple, 
and some are complex. Complex process¬ 
es should have formal training and be fol¬ 
lowed up by mentoring. 

Usage Problem and Solutions 

How can an organization afford to pro¬ 
vide three versions of the same documen¬ 
tation? Someday software could allow this 
by just setting a documentation mode 
(expert/intermediate/beginner) and the 
user could see the appropriate informa¬ 
tion. A best practice that solves this prob¬ 
lem is to define the process in chunks at the 
intermediate level (i.e., one version in 
intermediate mode). Add in training for 
the beginner and the expert can grab the 
appropriate chunks. Another best practice 
is that short, expert-mode documentation 
can also be provided for the experts. 

What Is a Good Process? 

The purpose of this section is to describe 
the required process elements necessary to 
define a good process. Table 4 (page 27) 
describes the practical who, what, where, 
when , why, and how process questions [2]. 
Each question is answered by a key 
process element. By addressing all the 
process elements on one page, the five W’s 
(who, what, where, when, and why) can be 
represented in a diagram on one page in 

expert mode (Figure 2). 

A good process should include the fol¬ 

• Address the five W’s and answer the 
key process questions. 

• Have both pictures and words (most 
people prefer pictures, but some peo¬ 
ple prefer words). 

• Be usable and well written. 

• Be well chunked and labeled so chunks 
can be found quickly [1]. 

• Be short (e.g., a diagram that answers 
the five W’s on one page). 

A Best Practice: Process 

A good process should have pictures. It is 
said that a picture is worth a thousand 
words. However, not all pictures are good 
pictures. Some pictures cause confusion, 
and some pictures are more harmful than 
helpful. So what is a good picture? Process 

modeling is a best practice that helps 
design good diagrams that address the five 
W’s. For a short and usable example, see 
Figure 2. 

What is a process model? A process 
model M models R (reality) if M answers 
questions about R [4]. A good process 
model should answer the key process 
questions (i.e. the five W’s in Table 4). A 
process model is typically represented by 
diagrams and powerful notations that rep¬ 
resent roles, activities, work products, and 
the relationships between them. 

What Is a Good Procedure? 

A good procedure consists of how-to, step- 
by-step information, and comes in three 
forms: checklists, forms, and step/action 
tables [3]. 


Checklists are powerful repeatable repre- 

Figure 1: Types of Process Documents and Their Ps£lationships 

Documentation Framework 

Note: Slide adapted from "A Software Process Famework for the SEI Capability Maturiy Model." Olson, et al. CMU/SEI-94-HB-01 

June 2006 25 

Software Engineering Technology 

Outputs/Exit Criteria 

■ Project CM Plan matches CM standard in Figure 1. 

■ Project CM Plan is reviewed and approved. 

■ CCB and CM Lead are identified. 

■ CM System is set up according to the set-up CM System Procedure. 

Figure 2 : 7.1 Develop Project Configuration Management Plan (Expert Mode) 



Process Step 



Assign Project CM Plan to CM Lead 

At the appropriate time in the project (typically during project planning), the PM assigns the Project's 

CM plan development to the CM lead. 


A CM lead should have experience in setting up CM systems and performing CM. The CM Lead should 
also have been trained in CM. 




Develop Project CM Plan 

The CM lead develops the Project CM plan according to the sections in the CM Plan Standard. It is required 
to follow the exact format of the CM Plan Standard 


The CM Plan Standard and example completed Project CM plans can be found on the Organizational 

Process Asset Web site. 





Review Project CM Plan 

The PM, CM Lead, and QA are required to peer review the CM Plan according to the CM Plan checklist in 
Appendix C. QA should review the CM plan against the CM Plan Standard. 


It is recommended to include the Project Team in the review. 




Set Up Project CM System 

The CM Lead sets up the CM system for the project according to the Set-Up CM System Procedure in 

Appendix D. This step should be performed concurrently with steps 7.1.5 and 7.1.6. 


This task is only done once and may already be completed for maintenance projects. 




If "YES," then proceed to Process Step 7.1.6. 

If "NO," then procedd back to Process Step 7.1.2 for rework. 

After the third "No" iteration or 2 weeks after the first disapproval, escalate a CM Plan disapproval issue to 
Project Management. 


QA should ensure the CM Project Planning Process is followed. QA also needs to work with the Project 
Manager regarding approval issues. 



Approve Project CM Plan 

QA approves the Project CM Plan and places the plan on the project's official Web site. 

This sub-process is not complete until all steps have been completed (e.g., 7.1.4). 


QA should ensure the Project CM Plan is integrated into the overall Project Plan. The Project CM Plan 
is placed under CM with the Project Plan in the Project Planning Process. 

Note: Project Manager (PM), Configuration Management (CM), Quality Assurance (QA). 

Table 3 : 7.1 Develop Project Configuration Management Plan (Intermediate Mode) 

sentations of activities that need to be 
completed to declare something complet¬ 
ed. What makes checklists so powerful is 
that it usually does not matter in what 
order the checklist is completed. This is 
why checklists are useful for concurrent 
activities (e.g., versus flowcharts that are 
poor at representing concurrency). 


Forms, along with instructions for com¬ 
pleting the forms, are repeatable mecha¬ 
nisms for supporting processes. Forms are 
powerful mechanisms for collecting data 
in a repeatable way. 

Step/Action Tables 

One effective way to represent a proce¬ 
dure is using a step/action table [1]. 
Step/action tables are useful when order 
matters. For example, if a person needs to 
track his or her time, then starting to track 
time should not be the last step. For an 
example procedure of when order mat¬ 
ters, see Table 5. 

Some Success Stories 

The best practices discussed in this article 
have been successfully used in practice 
over the last decade. More recently, major 
breakthroughs in defining extremely short 
and concise processes have also been 
achieved. An example of a short sub¬ 
process can be found in Figure 2 and 
Table 3. The following are some success 
story summaries (without revealing orga¬ 
nizational identities). 

Organizational Example I 

Organization No. 1 had a process that was 
not followed very well, and the process 
user feedback was that users did not like 
the process. The process had the follow¬ 
ing weaknesses (which are typical to most 

• Mixture of document types: policies, 
standards, processes, and procedures. 

• The existing processes lacked pictures 
(i.e., mostly text). 

• The principle of chunking was violated 
in the flowcharts and in the number of 
process and procedure steps (e.g., 
processes and procedures with more 
than 20 steps), making the processes 
and procedures hard to use. 

• Processes did not address all the five 
W’s (e.g., when was missing; some other 
W’s were also weak). It is hard to fol¬ 
low a process if you do not know 
when to start or when you are done. 

• Procedures were very large and hard to 
follow (e.g., typical of International 
Organization of Standardization [ISO] 

26 CROSSTALK The Journal of Defense Software Engineering 

June 2006 

Defining Short and Usable Processes 


New processes and procedures imple¬ 
mented the best practices described in this 
article and addressed all these weaknesses 
above. Process modeling was used to add 
good diagrams and address chunking of 
the flow chart. The new processes 
addressed all the five W’s and were defined 
on one page for expert mode (Please see 
Figure 2 for an example of the five W’s). 
In conclusion, organization No. 1 was 
much happier with diagrams on one page 
(i.e., process models), and with short, 
usable processes and procedures. 

Organizational Example 2 

In organization No. 2, although detailed 
procedures existed, the overall processes 
had not been documented. Four sub¬ 
processes were defined on one-page dia¬ 
grams (i.e., process models) for each 
process in expert mode (the five W’s on 
one page), and a page of text along with 
guidance was developed in intermediate 
mode to support the diagram (see Figure 
2 and Table 3 for a similar example). This 
organization packaged these four short 
processes in a single process guide in 
intermediate mode in about 20 pages total 
— four pages per process (similar process¬ 
es documents can be more than 100 
P a g es )- 

The experienced manager of this 
newly defined process was promoted in 
organization No. 2. Sometimes experi¬ 
enced people get stuck in positions 
because they are the only ones who know 
the processes, and often the processes are 
not documented. The new process docu¬ 
ment also helped with the transition 
because it allowed a new manager to come 
in and quickly learn the documented 

Organizational Example 3 

Organization No. 3 requested a process 
review of all its processes that identified 
strengths, weaknesses, and made specific 
recommendations to senior management. 
During the organizational process review, 
it was discovered that a key process was 
not documented. The process was 
designed into three sub-processes, and 
each sub-process was defined on one page 
using a diagram (i.e., expert mode), and a 
page of text (along with guidance) was 
developed in intermediate mode to sup¬ 
port the diagram (see Figure 2 and Table 3 
for a similar sub-process example). The 
entire new process guide (including the 
policy, standard, process, and procedures) 
is about 20 pages long total (this includes 
about a dozen sections, including purpose. 

Key Process Question 

Process Element 

Why is the activity performed? 

1. Purpose 

What actions are performed? 

2. Activities 

What work products are used? 

3. Input(s) 

What work products are produced? 

4. Output(s) 

When does the activity begin? 

5. Entry criteria 

When does the activity end? 

6. Exit criteria 

Who performs activities? 

7. Roles 

Where is activity performed? 

8. Process Context (e.g., Hierarchy) 

How is the activity implemented? 

9. Sub-Activity or Procedure 

Table 4: The Tradical Process Questions 

audience, usage, scope, metrics, proce¬ 
dures, references, etc). The new process 
also met all ISO requirements, which 
added about five pages. In conclusion, • 
organization No. 3 is much happier and 
now has more complete, better, shorter, 
and more usable processes and proce¬ 

Organizational Example 4 

Organization No. 4 complained that its 
processes and procedures were too large 
and difficult to use. After applying the best 
practices described in this article, the 
processes and procedures were cut in half • 
(e.g., 600 total pages were reduced to 300 
total pages). The processes and proce¬ 
dures are also more usable. What is fasci¬ 
nating about this example is although the 
processes and procedures were cut in half, • 
no information was lost. 

Some Lessons Learned 

Here are some of the lessons learned • 
while defining processes with best prac¬ 

• Do not mix policy, standard, process, 
and procedure information (e.g., in the 

Table 5: TLxample: Step I Action Table Procedure 




Begin to track time (e.g., write down the start time). 


Look for defects in the selected work product by using the 
appropriate data-driven checklist. 


Log the defects of the Defect Form. Continue logging defects 
until the work product is completely inspected using the checklist. 


End tracking time (e.g., write down the end time). Calculate the 
total time spent looking for and logging defects, and record the 
total time on the Defect Form. 

same paragraph). Label this different 
information, and consider how the 
information is used. 

Define all process documentation as 
simply as possible, but no simpler 
(information that is too simple does 
not work). Keep process documenta¬ 
tion concise (i.e., short and sweet), but 
expect some processes to be complex. 
Use good pictures (most people prefer 
pictures). Process modeling is a best 
practice and scales up to very complex 
systems. Use process modeling to 
develop good pictures. 

For each process or sub-process, 
define the five W’s on one page using 
a diagram (see Figure 2 for an exam¬ 
ple). A good process diagram can 
replace 20-25 pages of text. 

Use procedures (i.e., checklists, 
forms, and step/action tables [1]) for 
implementing processes and for 

Use chunking (i.e., seven plus or minus 
two), organize the chunks, and label 
the chunks (so users can find infor¬ 
mation quickly). Process modeling 
and information mapping [1] help 

June 2006 27 

Software Engineering Technology 

tremendously with this principle. 

• Account for beginner, intermediate, 
and expert users of the process. 

• Design measurement into the process. 
Do not add measurement as an after¬ 

• The processes must be tailored to each 
organization, each business unit or 
division, and each project. 


In summary, the objectives of this article 
are the following: 

1. Describe common problems with 
process documentation, including 
some human aspects of using process 

2. Discuss some best practices for defin¬ 
ing short and usable processes and 

3. Describe some success stories in real 

4. Provide some lessons learned. 
Defining short and usable processes 

and procedures is challenging. There are 
many best practices that can be used to 
help improve process documentation. The 
approach summarized in this article uses a 
collection of best practices, all wrapped 
into a process (for defining processes). 
The author hopes that the readers have 
benefited from the description of some of 
the best practices along with the example 

process in Figure 2 and Table 3.^ 


1. Chunking example: Most people can 
only remember chunks of information 
(e.g., that is why 15-16 digit credit 
cards numbers are broken into smaller 
chunks [seven, plus or minus two]; a 
16-digit Visa number is usually broken 
into four groups of four digits). 


1. Horn, Robert E. Mapping Hypertext: 
Analysis. Linkage, and Display Knowl¬ 
edge for the Next generation of On- 
Line Text and Graphics . Lexington, 
MA: The Lexington Institute, 1989. 

2. Olson, Timothy G., et al. “A Software 
Process Framework for the SEI 
Capability Maturity Model.” CMU/ 
SEI-94-HB-01. Pittsburgh, PA: Soft¬ 
ware Engineering Institute, 1994. 

3. Olson, Timothy G. “Defining 
Software Processes in Expert Mode.” 
Software Engineering Process Group 
Conference, Atlanta, GA, 1998. 

4. Ross, Douglas T., and Kenneth E. 
Schoman Jr. “Structured Analysis for 
Requirements Definition.” IEEE 
Transactions on Software Engineer- 
ingSE-3 (1) (1997): 6-15. 

About the Author 

Timothy G. Olson is 

founder and president of 
Quality Improvement 
Consultants, Inc. While 
performing quality con¬ 
sulting, Olson has helped 
organizations measurably improve quality 
and productivity, save millions of dollars 
in costs of poor quality, and reach higher 
Software Engineering Institute maturity 
levels. He has been formally trained in 
Crosby, Deming, Juran, International 
Organization of Standardization, Capa¬ 
bility Maturity Model® (CMM®), CMM 
Integration SM , and Six Sigma quality 
approaches. He is currently a senior mem¬ 
ber of the American Society of Quality 
and a member of the Institute of 
Electrical and Electronics Engineers. He 
has a master’s degree from the University 
of Massachusetts. 

Quality Improvement 
Consultants, Inc. 

71 17 Obelisco CIR 
San Diego, CA 92009 
Phone: (760) 804-1406 
Fax: (760) 804-1406 

Web Sites 

Earned Schedule 

Earned Schedule (ES) introduces the concept of adding ES 
into a project management repertoire. ES discusses the dif¬ 
ficulty of measuring schedule performance in dollars instead 
of units of time. ES is derived from Earned Value 
Management (EVM), but explores alternatives to EVM 
schedule tracking. It is derived from and is an extension to 
EVM. No additional data is needed for acquiring the ES 
measures; only the data from EVM is needed. In contrast to 
the cost-based indicators from EVM, the ES schedule per¬ 
formance indicators are time-based, making them easier to 
comprehend. The ES indicators provide a status and predic¬ 
tive ability for schedule, analogous to the facility for cost 
using EVM. 

Software Technology Support Center 

The U.S. Air Forces Software Technology Support Center 
(STSC) provides the Guidelines for Successful Acquisition 
and Management of Software Intensive Systems at 
<>. A 
streamlined version of the content is provided and broken 
up into chapters for desktop usable access, as well as an 

overview of important software acquisition and develop¬ 
ment topics, helpful checklists for rapid self-inspection, and 
acquisition and management of software pointers. The 
STSC also oversees CROSSTALK, offering monthly articles 
aimed at the software technology industry. 

Software Engineering Institute 

Carnegie Mellon University’s Software Engineering Institute 
(SEI) has had the national mandate to advance the state of 
the practice of software engineering and to serve as a nation¬ 
al resource in software engineering and technology. SEI 
aims to help organizations and individuals improve their 
software engineering management practices. SEI provides 
services dealing with the major categories of software engi¬ 
neering, including management, engineering and acquisi¬ 
tions. The Web site also provides links to education, train¬ 
ing, and frequently asked questions in its mission to support 
software engineering professionals and advance software 
engineering and related disciplines to ensure the develop¬ 
ment and operation of systems with predictable and 
improved cost, schedule, and quality. 

28 CROSSTALK The Journal of Defense Software Engineering 

June 2006 

Open Forum 

Should Your Projects’ Leaders Be on Springer? 

Paul Kimmerly 

U.S. Marine Corps ■ Technology Services Organisation 

This issue of CROSSTALK focuses on software projects that fail Process improvement programs can help a software pro¬ 
gram succeed. They can also help itfail. Process improvement programs have the potential to be dysfunctionalfor their own rea¬ 
sons. To help put this in perspective, I chose to look at the ringmaster of dysfunction, jerry Springer. This article draws a par¬ 
allel between the problems faced by process improvement programs to the experiences related on The Jerry Springer Show. 

P rocess improvement programs face a 
number of challenges within an 
organization. These challenges can 
include fickle or uncaring sponsors, 
strange behavior, and process envy. Such 
dysfunctional behaviors reflect the per¬ 
sonalities and culture of the organiza¬ 
tion. Any organization is a miniature 
version of society as a whole. In order to 
gain some insight into the effects of dys¬ 
functional behavior in an organization, 
let us turn to the best demonstration of 
dysfunction in our society. The jerry 
Springer Show. This article will relate 
some of the dysfunctional behavior seen 
on the Springer show that sabotages rela¬ 
tionships to the resistance and strange 
behavior that can sink a process 
improvement program. 


Many articles and presentations on suc¬ 
cessful improvement programs talk 
about the importance of sponsorship. 
Involved and active sponsorship is criti¬ 
cal to process improvement efforts. If 
the sponsor gets involved, others in the 
organization notice and play along. 
There are two types of sponsor behav¬ 
iors that can hurt an improvement 
effort: cheating and a lack of attention. 

The Springer show routinely features 
cheating spouses. Invariably, they bring 
their new lover on the show and a fight 
ensues. Feelings are hurt and the rela¬ 
tionship is never the same. In the 
process improvement world, cheating 
spouses relate to sponsors that jump 
from improvement idea to improvement 
idea. Reading too many airline maga¬ 
zines often causes this problem. A spon¬ 
sor will profess his love for Capability 
Maturity Model Integration SM (CMMI®)- 
based process improvement one day, 
staff an improvement program, and ask 
his managers to join in. Then, while the 
sponsor is on a business trip, a new 
improvement program catches his eye. 
Maybe it is Lean Six Sigma. Maybe it is 
the Balanced Scorecard. Suddenly that 

CMMI book just does not look the 
same. Its cover isn’t as shiny and his 
attention wanders. The rest of the orga¬ 
nization will notice this and follow his 
lead. This can lead an organization down 
a long road of shifting from one 
improvement effort to another, and it 
does not take long for the organization 
to see improvement efforts as shallow 
and pointless. It takes the full commit¬ 
ment of the sponsor to win the hearts 
and minds of the organization. 

“The sponsor pays 
close attention while the 
process is growing up, 
but can easily become 
content once the target 
grade level is achieved. 
The sponsor may forget 
that the improvements 
came about because of 
their involvement. ,, 

Even if sponsors remain committed to 
a single improvement program, they can 
lose focus. If that happens, subordinate 
managers who never really bought into the 
improvement program will see an oppor¬ 
tunity to resist changing their behavior. 
Organizations that focus on the grade for 
a CMMI appraisal are especially suscepti¬ 
ble to this behavior. 

Another regular event on the Springer 
show involves parents who are suddenly 
confronted with the unexpected and wild 
behavior of their children. They say things 
like, “He’s always been such a good child.” 
Then the child comes out dressed as a 
vampire or wearing a diaper and brings 

out his or her equally bizarre friends. At 
one time, these were probably good, atten¬ 
tive parents. However, as the children 
grew older and started hanging out with 
their friends and pursuing other interests, 
the parents felt their job was done and 
turned to other interests of their own. 

This type of inattentive parenting 
equates to sponsors and managers who 
rush to put improvements in place to get 
the grade. The sponsor pays close atten¬ 
tion while the process is growing up, but 
can easily become content once the tar¬ 
get grade level is achieved. The sponsor 
may forget that the improvements came 
about because of their involvement. 
Once the grade is achieved, the sponsor 
may turn attention to more pressing 
matters with the expectation that things 
will not change. Project managers not 
committed to continuing improvement 
will begin to stop following the process¬ 
es they do not like or do not see as 
important. While the sponsor is saying, 
“It has always been a good project. It’s 
Level 3, you know,” things suddenly start 
to change. The project starts to miss 
deadlines, and status reports lose their 
clarity and validity. Customers start com¬ 
plaining and the sponsor cannot under¬ 
stand what went wrong. Sponsors need 
to continue to stress the importance of 
good processes and support their 
Quality Assurance (QA) group, which 
can often be the first source of informa¬ 
tion when a project’s behavior turns 
strange. A well-established and support¬ 
ed QA group can be a surrogate parent 
to make sure the projects continue to 
follow their processes as intended. 

Project Managers 

Project managers play an integral role in 
continuing improvement efforts. 
Successful improvements require cas¬ 
cading sponsorship that flows from the 
sponsor through the higher levels of 
management down to the practitioners. 
Unfortunately, many mid-level managers 
resist improvement and the changes 

June 2006 29 

Open Forum 

associated with it. 

On the Springer show, many of the 
conflicts stem from the refusal of one 
person to commit to a long-term rela¬ 
tionship. Promises of marriage never 
result in a trip to the altar. Eventually, 
one of the people in the relationship 
strays. Then, they show up on Springer 
with a new love (or two) and a fight 
ensues. Managers who resist often say 
the right things in the presence of the 
sponsor, but never fully commit to mak¬ 
ing improvements. Like the proposal 
that never comes, the improvements are 
constantly delayed. Some small change 
may be made when sponsors are look¬ 
ing, but the managers never commit 
when left on their own. If the sponsor 
does not press the issue, the desired 
change will not happen. 

Cross-dressers often make appear¬ 
ances on the Springer show. These people 
present one face to the public while 
behaving very differently behind closed 
doors. The Springer show lets them 
expose their true selves on national tele¬ 
vision. Projects often behave the same 
way. It is not unusual for a project to 
develop a lot of process documentation 
and store it on their organization’s 
repository for all to see. They might 
even make a big show of producing the 
documentation. The public face looks 
the way it should; however, when a clos¬ 
er look is taken, the documentation is all 
for show. The project continues to 
behave the way it always did without 
making the changes that were docu¬ 
mented. This often happens when a pro¬ 
ject tries to write documentation that 
matches the way it thinks things should 
be done, not the way they are done. Most 
organizations have some level of infor¬ 
mal process in place to produce soft¬ 
ware. It may not be written down, but 
people know the general way that things 
are done. An easy first step in an 
improvement program is to document 
the current process and then look at 
where improvements are needed. 
Projects that want to look like they are 
doing the right thing often take a differ¬ 
ent approach. They give someone the 
task to go off in a corner and write the 
documentation without involving the 
people performing the work. When that 
happens, the documentation looks great. 
Unfortunately, no one follows it because 
they have no stake in what it says. 

Resistance to change is to be expected 
in life and in process improvement. Some 
resistance is more obvious. When people 
resist the changes in their relationship, 
chairs fly across the stage at the Springer 

show. In organizations, some projects are 
just as open in fighting change. Compared 
to hidden resistance, this kind of resis¬ 
tance is easier to handle because it is so 
obvious. On Springer , Steve and the securi¬ 
ty crew know to rush the stage and get 
between the combatants. In an organiza¬ 
tion, the sponsor and the process improve¬ 
ment group need to know where to step in 
and take action. 

Sometimes, Springer reveals secret 
crushes where one guest longs for 
another, but fears to come out in the 
open. This can actually be a good situa¬ 
tion for an organization. If a sponsor 
praises a project with proven, successful 
processes, other projects will want simi¬ 
lar attention. They will secretly crave the 
sponsor’s attention. Such feelings can be 
leveraged to bring improvements to 
those projects. 

Improvement Groups and 

Steve and his security staff sit right at 
the edge of the Springer stage ready to 
jump in and get in the middle of any 
problems that arise. Process improve¬ 
ment groups serve the same role in an 
organization. When unexpected conflict 
or resistance appears, the process group 
is there to step in and work with all the 
involved parties to reach a solution. 
Sometimes the combatants on Springer 
do not want to stop, just like projects 
that always resist. Steve and his crew 
keep coming back until the guests 
behave. Process improvement groups 
and sponsors need that same level of 
commitment and persistence. 

At the end of every show, Jerry sits 
off to the side and provides a little 
homily. He summarizes what the audi¬ 
ence saw and puts it all into perspective 
as much as possible considering what is 
usually on the show. Lead appraisers fill 
that same role in improvement efforts. 
What they see in an organization may be 
as ugly as what Jerry deals with every 
day, but they are supposed to stay above 
it all and put the organization’s behavior 
into perspective. That is, of course, 
unless the lead appraiser has been work¬ 
ing as a paid consultant with the organi¬ 
zation on its improvement efforts. That 
relationship can be a little too close, like 
some of the family members that show 
up on the Springer show. Such behavior 
from consultants may produce mislead¬ 
ing or questionable appraisal results. 


Through it all, Jerry and his security 

staff see the problems of society pass in 
front of them every day. They see all 
manner of people from society’s main¬ 
stream to its fringes. Now, this article is 
not intended to encourage process 
improvement groups to watch The Jerry 
Springer Show for guidance or insight, 
even though all of the behaviors listed 
here can probably be seen in a one-hour 
episode. Watching the show is its own 
form of dysfunctional behavior. 

In an organization, a process 
improvement group is likely to get a 
Springer-zsogxz view of behavior from 
dealing with all levels of the organiza¬ 
tion. Dysfunctional behavior can come 
from the sponsor, the middle managers, 
or even the practitioners. Whatever the 
source or nature of the behavior, it can 
cause a process improvement program 
to fail. Failed improvement programs 
can have a larger affect on the organiza¬ 
tion as a whole. The Springer show puts 
extreme dysfunctional behavior on the 
public stage where we can all see its 
effects. Improvement groups and 
appraisals can do the same for an orga¬ 
nization by showing the dysfunctional 
process behavior to the sponsor and 
working to address it.^ 

About the Author 

Paul Kimmerly has 17 

years experience in soft¬ 
ware development for 
the different incarnations 
of the U.S. Marine Corps 
Technology Services Or¬ 
ganization in Kansas City. A member of 
the Software Engineering Process Group 
(SEPG) since 1993, Kimmerly has 
served as the group’s chair for the past 
nine years. Paul is an authorized Standard 
CMMI Assessment Method for Process 
Improvement Lead Appraiser. He pre¬ 
sented at the 1997 and 2000 Software 
Engineering Symposiums and the 2004 
National SEPG Conference. Kimmerly 
has contributed several articles on 
process improvement to CROSSTALK 

1500 E 95 ST 
Kansas City, MO 64197 
Phone: (816) 926-5364 
DSN 465-5364 
Fax: (816) 926-6969 
DSN 465-6969 


30 CROSSTALK The Journal of Defense Software Engineering 

June 2006 

• • • 


When Failure IS an Option 

I ’m sitting here writing this BackTalk while on business in 
Baltimore. I flew into the Baltimore Washington International 
Airport (BWI). I remember a few years ago when BWI advertised 
itself as a great alternative to both Reagan National (in down¬ 
town D.C., always crowded) and Dulles (which is about 25 miles 
out of D.C.). BWI was convenient to both Baltimore and D.C., 
and small enough that rental cars were located within a five 
minute walk of the terminal. Things have changed! BWI is now 
under construction, and I had to walk from one end of the air¬ 
port to the other to get to baggage claim. Then, I had to walk all 
the way to the other end of the airport to catch the “rental car 
bus.” Car rentals are now about five miles away, so you have no 
option other than the inconveniently located rental car bus — and 
the buses were extremely crowded. Things change. What used to 
be a good thing becomes inconvenient and appears to be poorly 

Which brings us to “Why Software Fails.” This has been a 
hard column to write — I was tempted to take the easy way out, 
and simply list and add the pictures of a few former co-workers 
and acquaintances who, in my opinion, have contributed to fail¬ 
ing software over the years. 1 But instead, I have come up with a 
good start at a list that explains why software fails. 

Software fails ... one day at a time. Insidious little events 
occur. Small errors creep in. You have the road not taken syn¬ 
drome. You realize that you could do better, but you don’t have 
time to go back and start all over again. It’s not always the big 
errors that cause failure, it’s the little errors that accumulate. 

Software fails ... with the best of intentions. Developers, 
with the exception of a few TRULY unspectacular folks I have 
known, don’t really set out to do a poor job. 2 We try and make the 
right choices, but we don’t have the ability to predict the future. 
If things had turned out a little differently, we would have had a 
spectacular success. Instead, decisions turned out to be sub-opti¬ 
mal. If we only had the time to do it over. 

Software fails ... because we have no other choice. 
Sometimes politics, budgets, and schedules force us to make deci¬ 
sions we don’t like. In a perfect world, we would have the time 
and budget to make perfect software. The world isn’t perfect, and 
we are often forced into less than perfect solutions. We know bet¬ 
ter, we just can’t do better. Real-world requirements change and 
we have to make the software react also, or the software becomes 
obsolete. I am relatively sure that the designers of the new BWI 
rental car terminal wish they were still located within a few hun¬ 
dred feet of the airport. However, the reality is that increased air 
traffic and congestion made this option infeasible. Sad to see it 
go, but it beats NOT having a rental car, doesn’t it? 

Software fails ... because we are overcome by events. 
Sometimes, we have to make choices before we have time to 
research all of the options. Schedules are tight and it’s more 
important to make a workable decision now rather than making 
a better decision later. We don’t like it, but it’s just what we have 
to do. 

Software fails ... because we can’t think of everything. Ever 
left the house for the grocery store with a memorised grocery list? 
You started out for milk and eggs. You added carrots and sliced 
cheese. Your spouse reminded you that you need toothpaste and 
shampoo. Only a few items. Yet, by the time you get to the gro¬ 

cery store, you’re reduced to calling home, because all you can 
remember is milk, eggs, and something else. How many things can 
you juggle in your memory at one time? For most people, I sus¬ 
pect this number peaks out around nine or 10. Unfortunately, 
large-scale software has millions of lines of code, and literally 
tens of thousands of function points. How can we comprehend 
such large scale? We use architectural design to decompose the 
problem, and we use high-level languages, modularity, and object- 
oriented techniques to further break the problem down. 
However, with software of such large size, things just slip 
through the cracks. Requirements are missed or not implement¬ 
ed. Obvious errors are usually obvious only in hindsight — after 
the failure. 

Software fails ... because developers are only human. Have 
you ever spent hours (or even days) looking for an error, and had 
somebody wander by, glance over your code, and immediately see 
the problem? When you develop code, you tend to internalize 
your own errors, and then your brain fails to see them. An out¬ 
side observer, however, can often see what you keep overlooking. 
Almost all good developers know that you need somebody else 
to review your work. This applies to all phases of software devel¬ 
opment: requirements, design, coding, and maintenance. One of 
my favorite quotes is, “When quality is vital, independent checks 
are necessary, not because people are untrustworthy but because 
they are human 3 .” Even if you are one of the best software devel¬ 
opers around, 4 you make mistakes. So does everybody else. 

Software fails ... because you failed to consult the Software 
Technology Support Center (STSC) for help when developing 
your software. Or, if not the STSC, you should learn from some¬ 
body. You want to emulate the best practices of others while at 
the same time keep from making the same mistakes that others 
have made. Learn from the mistakes of others and also learn 
from the success of others. Find out what other similar develop¬ 
ment efforts did right and wrong. Read journals. Talk to fellow 
developers on other projects. But then — you are already reading 
Crosstalk, aren’t you? 

— David A. Cook, Ph.D. 

The AEgis Technologies Group, Inc. 

P.S. I am not claiming my list is complete or even valid (after all, 
I didn’t review this with anybody else!). Feel free to e-mail me 
your additions or comments, and maybe you’ll see them in a 
future BackTalk column. 


1. Worried that you’ll find your name here, aren’t you? 

2. STILL worried that you’ll find your name here, aren’t you? 

3. Humphrey, Watts S. Managing the Software Process . 

Addison-Wesley, 1989. 

4. Well, you certainly aren’t expecting to find any name other 

than mine, are you? 

June 2006 3 I 

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