MEASURING REQUIREMENTS COMPLEXITY TO INCREASE THE
PROBABILITY OF PROJECT SUCCESS
Holly Parsons-Hann, Kecheng Liu
Informatics Research Centre, The University of Reading, Reading, Berkshire, RG6 6AY, UK
Keywords: Project Management, Requirements complexity
Abstract: The widespread adoption of Information Technology has helped reduce market problems caused by
geographical separation and allow collaboratio
n between organisations who are physically distributed
around the globe. However, despite the successful strategic benefits brought by the evolution of the Internet
and other web based services, this has not led to a higher project success rate. The biggest reason for project
failure is cited as ‘incomplete requirements’ which suggests that research must be done into the requirements
analysis to solve this reoccurring problem. This paper aims to highlight and analyse the current work done in
the software complexity and requirements engineering field and demonstrate how measuring requirements
complexity will lead to less project failures.
1 INTRODUCTION
Due to changing demands and technological
advancement, businesses respond by developing
increasingly flexible infrastructures in order to
remain competitive in the newly defined market
boundaries. However, despite adapting their
business practices to exploit newly discovered
market niches, organisations are still finding it
increasingly difficult to employ successful project
management techniques. One of the most famous
project software statistics is attributed to the
Standish Group’s 1995 CHAOS report which states
that 31.1% of projects will be cancelled before they
ever get completed (Chaos 1995).
Recent investigations have demonstrated that the
si
tuation has not improved in the last 10 years
despite the release of various project management
tools and methods. A survey conducted by Taylor
in 2001 purports that out of 1027 software projects
only 12.7% were considered successful (Taylor
2001). The reasons for project failure are widely
documented and known, however there is no
standard list of reasons due to the heterogeneity of
the organisations in question. The CHAOS report
lists the number one reason for inability to complete
a project as ‘incomplete requirements’, stating that
this is true in 13.1% of the organisations
interviewed.
The structure of the paper i
ntroduces the current
work performed in both the software complexity
and requirements engineering area followed by the
identified factors affecting the requirements
complexity. The research is then presented with
conclusions and future work at the end of the paper.
2 CURRENT WORK IN THE
SOFTWARE COMPLEXITY
AREA
Software complexity is a well known paradigm
within the software engineering domain and one
which boasts a rich supply of metrics claiming to be
able to define and measure the ‘complexity’ of
software. Within this area, different types of
complexity have been identified and defined in an
attempt to measure productivity, dependence,
testing and maintenance effort.
Inheritance complexity is measured using the depth
of
inheritance tree, whereas standalone complexity
is measured by the weighted methods per class
metric (Chidamber 1994). Interface complexity.
Cyclomatic complexity (McCabe 1994) is the most
widely used and recognised software metric which
may be considered a broad measure of the
434
Parsons-Hann H. and Liu K. (2005).
MEASURING REQUIREMENTS COMPLEXITY TO INCREASE THE PROBABILITY OF PROJECT SUCCESS.
In Proceedings of the Seventh International Conference on Enterprise Information Systems, pages 434-438
DOI: 10.5220/0002548104340438
Copyright
c
 SciTePress

soundness and confidence for a program. it
measures the number of linearly-independent paths
through a program module is often used on concert
with other software metrics.
One of the problems with measuring complexity
and using the metrics described above is the lack of
a working definition with intuitive notions and
assumptions leading to contradictions thus
confusion. In this paper, we are using the Oxford
English Dictionary’s definition of the word
‘complex’ which states that
From this definition we have attempted to define all
the different parts e.g. cost drivers which will have
an effect on the complexity of requirements.
3 REQUIREMENTS
ENGINEERING
One of the biggest problems with requirements
engineering is that the stakeholders may be
numerous and distributed with varying and
conflicting goals. Eliciting stakeholders
requirements is a challenge in itself (Holtzblatt et
al. 1995) (Goguen et al. 1993) however once
collected there is the task of articulating the
requirements to be considered. Once collected, the
requirements must be expressed into a common
language understandable to both the project team
and the stakeholders.
By adopting new requirement engineering
techniques, it is hoped that stakeholders
requirements will be better understood and
modelled and the cultural behaviours of
stakeholders better understood (Liu 2000).
Therefore we propose a complexity measure that
will take the natural language requirements as input
and associate a value of complexity to each one.
The greater the value, the harder project success
will be to obtain and therefore more caution must
be exercised when implementing such projects.
4 FACTORS AFFECTING
COMPLEXITY
The concept of complexity is in itself a ‘complex’
notion. There are many factors which contribute to
the level of complexity and make each requirement
a more or less complex objective. We have
identified the cost drivers we feel are responsible
for affecting the complexity of requirements and
attempt to define each one below.
Time spent on the project
Estimating how many hours a task can take an
employee is not an easy job. There are many factors
to take into account such as the skills and
experience of the employee, the resources available
and the difficulty of the task in question. If the task
is relatively small, fairly accurate estimations can
be made, for example ‘How long will it take for
employee X to write a 3 page report on
requirements bearing in mind all the stakeholders
are in the same office as that employee’ One could
estimate that the task would not take more than a
day, therefore 7/8 hours and be accurate to within 1
or 2 hours.
Complex (adj)
1. Consisting of many different and connected
parts
2. Not easy to analyse; complicated or intricate
On the other hand estimating how long it will take
an employee to build a modular component for an
Information System when a lot of factors are
undetermined is a lot harder. However due to
project deadlines an estimation still has to be made.
Should the project manager/ project team be
experienced in the building of such systems, they
may already have some knowledge as to how long
such a module usually takes based on past
experiences and the types of problems associated
with the task. This experience is invaluable as it can
sometimes bring very accurate estimations using
limited data available.
On the other hand, each project is an individual and
therefore what went right for one project will not
necessarily go smoothly for the next project, thus
basing estimations on past experience is not always
the best idea. Therefore an equation needs to be
used to estimate the number of hours spent on a
particular task as accurately as possible.
Heterogeneity of the Organisation
Due to the IT sector being a highly fragmented and
competitive industry, organisations must be both
aggressive and flexible yet still maintain a good
communication infrastructure. This can prove
challenging when forming partnerships with
MEASURING REQUIREMENTS COMPLEXITY TO INCREASE THE PROBABILITY OF PROJECT SUCCESS
435

geographically dispersed organisations as chances
are both business and cultural norms will differ
greatly. This problem is magnified if we imagine
one company to be in the UK and the other to be in
America as due to the difference in time zones the
working week is significantly shortened.
Research has been done into a number of
‘capability barriers’ which prevent effective
communication in geographically dispersed groups
(Toomey et al. 1998). The three identified problems
included not sharing a common first language,
being separated by sixteen time zones and the
difference in typing ability when communicating
via a messaging program.
Skill of project members
According to Wrigley and Dexter (Wrigley et al.
1987), expert judgement is still the most dominant
method of estimation. This is supported by
Vicinanza et al. (Vicinanza et al. 1991) who
concludes that experienced managers can make
more accurate estimates than existing uncalibrated
algorithmic models, specifically COCOMO or
function point analysis. Simon stated that ‘Every
manager needs to be able to respond to situations
rapidly, a skill that requires the cultivation of
intuition and judgement over many years of
experience and training’ (Simon 1987).
This is a widely accepted comment in industry who
realise that good managers have both business
acumen and business experience which has trained
them to react to the changing market demand.
However, very few project teams will just consist of
experienced high ranking employees. Most projects
will have an eclectic range of skilled employees
ranging usually from new graduates to the project
manager who will have a number of years
experience dealing with similar projects.
Number and location of stakeholders
A stakeholder is any individual or group which can
affect or is affected by an organisation’s activities,
employees are a key group. Individually they can
enhance sustainability at the company they work for
by bringing their personal skills and experiences to
aid change and innovation.
When conducting a project for a Virtual
Organisation there is a strong likelihood that some
of the stakeholders will not be located in one
geographic site. This means trying to elicit the
requirements from numerous different stakeholders
from many different locations.
Project Resources
A resource can be defined as ‘something that is
used for support or help’, therefore we can
understand a project resource to be ‘a means
available to the company to help meet its’
objectives’. Money, employees and skills are all
examples of project resources as they help the
project to be successful and meet all the objectives
on time and accurately. However, as stated by the
CHAOS report the third biggest reason for project
failure was due to lack of resources which 10.6% of
all the companies interviewed stated.
By ascertaining and measuring the amount of
resources available to the project at the inception
stage of the lifecycle we believe it will contribute to
the overall requirements complexity value.
5 OUR RESEARCH
From the factors identified above, measurements
have been created which, when finished will
attribute a numerical value to each requirements in
terms of complexity, the lower the value, the less
complex the requirement is seen to be. When
creating a metric to measure the time spent to fulfil
the requirement, we assume the following
statements are true:
1. The larger the estimation the less accurate
experienced guesses will be;
2. The earlier the estimate is made, the
greater the estimating errors (Jorgensen
2002).
Logarithms have been chosen due to their ability to
be able to analyse exponential processes. As the log
function is the inverse of the exponential function it
is possible to measure and model many natural
processes such as the acidity of a chemical solution
or earthquake intensity on the Richter scale. As
historical data is currently regarded as the best
source of estimating projects (Boehm 2000) the
data used must be accurate and complete (Fairley
2002).
Due to this increasing error risk, the metric created
has been on a logarithmic scale as previous research
has shown range metrics to be more accurate
(Jorgenssen 2002) (Fairley 2002). Figure 1.
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0
0
Complexity Number
Fi
g
ure 1: Hour com
p
lexit
y
metric
321-480
5
0-8
1
8- 40
2
3 4
41-160 1-160 161-320 161-320
Number of hours Number of hours
illustrates the metric created with a numeric value
associated to each range.
illustrates the metric created with a numeric value
associated to each range.
As most Project Managers prefer short incremental
steps in development, 480 hours e.g. 3 months was
chosen as the maximum number of hours allowed to
be estimated. Should the project team feel that a
requirement will take longer than 3 months to fulfil,
the value 5 can be attributed to that requirement and
should be flagged as a possible ‘high risk’
requirement.
As most Project Managers prefer short incremental
steps in development, 480 hours e.g. 3 months was
chosen as the maximum number of hours allowed to
be estimated. Should the project team feel that a
requirement will take longer than 3 months to fulfil,
the value 5 can be attributed to that requirement and
should be flagged as a possible ‘high risk’
requirement.
DifferentThe sameBusiness norms
DifferentThe sameTechnology used for communication
DifferentThe sameIndustry domain both organisations
are in
DifferentThe sameNative language of all stakeholders
8 +
Time
zones
0-8 Time
zones
Time zones the company crosses
10Property
DifferentThe sameBusiness norms
DifferentThe sameTechnology used for communication
DifferentThe sameIndustry domain both organisations
are in
DifferentThe sameNative language of all stakeholders
8 +
Time
zones
0-8 Time
zones
Time zones the company crosses
10Property
DifferentThe sameBusiness norms
DifferentThe sameTechnology used for communication
DifferentThe sameIndustry domain both organisations
are in
DifferentThe sameNative language of all stakeholders
8 +
Time
zones
0-8 Time
zones
Time zones the company crosses
10Property
As the heterogeneity of the organisation can also
contribute to the complexity of the requirements
due to ongoing liaisons with stakeholders, it is
important to acknowledge this fact and use a metric
to gauge how complex the requirements are likely
to be. Figure 2. gives a clear guide as to which
factors are likely to affect the requirements
complexity and the number which should be
attributed to the requirement after considering each
factor. When assessing each factor, the project team
should add the score in each row together so that
they get a value between the ranges of 0 and 5.
5New employee, no experience in
industry or projects
4New employee, experience in industry
but not in projects
3Employee who has been with the
company a short length of time, has
experience in 1-2 similar projects
2Competent employee, been with
company for length of time, previous
experience in similar projects
1Highly competent employee, been
with company for a long length of
time, previous experience in similar
projects
ValueSkill
5New employee, no experience in
industry or projects
4New employee, experience in industry
but not in projects
3Employee who has been with the
company a short length of time, has
experience in 1-2 similar projects
2Competent employee, been with
company for length of time, previous
experience in similar projects
1Highly competent employee, been
with company for a long length of
time, previous experience in similar
projects
ValueSkill
Figure 3: Employee skill metric.
Skill of the project members has also been
identified as a possible factor affecting the
complexity of user requirements. Although the skill
of members could be measured in a number of very
complex ways, only one factor has been highlighted
as being important: Has the project member worked
on a similar project before, and if so how many
similar projects has he/she been part of?
Figure 3. illustrates the skill complexity metric and
attributes a value to each project member out of 5.
Once all project members have been assessed each
value should be added up and divided by the
number of project members that are present. For
each metric a value out of 5 will be elicited. For
each metric used these numbers should be added
together and divided by the number of metrics used.
For example if the hour and employee skill metrics
were the only ones used, the value from both should
be added together and the answer divided by two to
gain a requirements complexity value.
Figure 2: Stakeholder metric
6 CONCLUSIONS & FUTURE
WORK
It is clear that requirements complexity contributes
to project failure in organisations, what is not
apparent is to what degree this statement holds true.
Future work will allow the creation of an effort
metric, a metric which takes into account the
number and location of stakeholders and the
amount of project resources available. Once the
metrics are completed they will then be validated by
being used in a number of different organisations
and the results published. It is hoped that by
MEASURING REQUIREMENTS COMPLEXITY TO INCREASE THE PROBABILITY OF PROJECT SUCCESS
437

measuring the complexity of user requirements it
should be possible to have a better understanding of
the issues which may cause problems later on in the
project lifecycle. By identifying these problems at
the first possible opportunity they can be dealt with
leaving more project opportunities available than if
dealt with at a later date.
REFERENCES
Boehm, Barry W., 2000. Software Cost Estimation with
COCOMO II. London: Prentice-Hall.
Chidamber, S, R, Kemerer, C, F, 1994. A Metrics Suite
for Object-Oriented Design, In IEEE Transactions on
Software Engineering, Vol. 20, No. 6, pp. 476-493
Delany, S., J., Cunningham, P., Wilke, W., 1998. The
limits of CBR in Software Project Estimation,
German Workshop on Case-Based Reasoning,
http://www.cs.tcd.ie/publications/tech-
reports/reports.99/TCD-CS-1999-21.pdf
Fairley, Dick, 2002. Making Accurate Estimates. In
IEEE Software, Vol 19 No. 6 pp 61-63.
Goguen, J., Linde, C., 1993. Techniques for
Requirements Elicitation. 1st IEEE International
Symposium on Requirements Engineering pp. 152-
164.
Holtzblatt, K., Beyer, H. R. 1995. Requirements
Gathering: The Human Factor. Communications of
the ACM, Vol 38, No 5, pp 31-32.
IT Cortex, 2004. Failure Causes – Statistics,
http://www.it-cortex.com/Stat_Failure_Cause.htm
Jorgensen, M., 2002. A review of Studies on Expert
Estimation on Software Development Effort, Simula
Research Laboratory, Norway
Liu, Kecheng., 2000. Semiotics in Informations Systems
Engineering, Cambridge University Press
McCabe, Thomas J. & Watson, Arthur H, 1994, Software
Complexity.
Crosstalk, Journal of Defense Software
Engineering, Vol 7
, No.12
Niessink, F., van Vliet, H., 1998. Two Case Studies in
Measuring Software Maintenance Effort, In
Proceedings of ICSW , pp 76-85
Simon, H., A., 1987. Making management decisions: The
role of intuition and emotion, In Acad. Management
Exec Vol 1. pp 57-63
Standish Group 1995 CHAOS report
Taylor, A., 2001. IT Projects sink or swim, BCS review,
http://www.bcs.org/review/2001/articles/itservices/pr
ojects.htm
http://www.doc.ic.ac.uk/~ban/pubs/sotar.re.pdf
Toomey, L., Smoliar, S., Adams, L,1998. Trans-Pacific
Meetings in a Virtual Space, FX Palo Alto Labs
Technical Reports
Vicinanza, S., T., Mukhopadhyay, T., Prietula, M., 1991.
Software effort estimation: an exploratory study of
expert performance, In Information Systems Research
Vol 2. No. 4, pp 243-262
Wrigley, C., D., Dexter, A., S., 1987. Software
development estimation models: A review critique, In
ASAC’87, Administrative Sciences Association of
Canada Conference, pp 125-138
ICEIS 2005 - INFORMATION SYSTEMS ANALYSIS AND SPECIFICATION
438