J. Software Engineering & Applications, 2010, 3, 852-857
doi:10.4236/jsea.2010.39099 Published Online September 2010 (http://www.SciRP.org/journal/jsea)
Copyright © 2010 SciRes. JSEA
A Review of the Impact of Requirements on
Software Project Development Using a Control
Theoretic Model
Anthony White
School of Engineering and Information Sciences, Middlesex University, the Burroughs, Hendon, London, UK.
Email: a.white@mdx.ac.uk
Software projects have a low success rate in terms of reliability, meeting due dates and working within assigned budg-
ets with only 16% of projects being considered fully successful while Capers Jones has estimated that such projects
only have a success rate of 65%. Many of these failures can be attributed to changes in requirements as the project
progresses. This paper reviews several System Dynamics models from the literature and analyses the model of Andersson
and Karlsson, showing that this model is uncontrollable and unobservable. This leads to a number of issues that need to
be addressed in requirements acquisition.
Keywords: Requirements Models, System Dynamics, Control Systems, Observability, Controllability
1. Introduction
Software projects have a low success rate in terms of
reliability, meeting due dates and working within as-
signed budgets [1-3] with only 16% of projects being
considered fully successful while Capers Jones has esti-
mated that such projects only have a success rate of 65%.
The American “Standish Group” has been involved for
10 years with research into ICT. In their research, they
aim to determine and change success and failure factors
regarding such projects. Their study, which has been
appropriately baptised “Chaos” [4,5], appears every two
years. This study also shows that in 2003 only 34% were
successful, 51% did not go according to plan but ulti-
mately did lead to some result and 15% of the projects
fail completely.
Despite these failures significant progress has been
made in the use of System Dynamics methods to describe
the development of software projects. The models of
operation of the software development process were de-
scribed by the successful System Dynamics (SD) models
based on the work of Abdel-Hamid & Madnick [6],
which set up equations relating levels such as the number
of perceived errors, or the number of reworked errors
and relates them to rates such as the error detection rate
or the rework rate, significant features of these models
included the decision processes. These models were
validated against NASA project data for a medium size
project and the agreement is strikingly good.
Many of these failures can be attributed to changes in
requirements as the project progresses. Capers–Jones [7]
states that as the project gets larger the probability of
requirements creep becomes more likely, typically 1-2%
per month and as high as 10% in a single month. Lorin
May [8] talks about poorly established guidelines that
determine when requirements should be added, removed
and implemented. Deifel and Salzmann [9] describe a
view of “requirements dynamics” relating to the process
of changing requirements. They go on to develop a
strategy to deal with the regime in which some require-
ments are invariant and some migrate.
Coulin et al. [10] state that “the elicitation of require-
ments for software systems is one of the most critical and
complex activities within the development cycle” and
that “this is preformed after project initiation and pre-
liminary planning but before system conception and de-
sign.” This would not be strictly true if evolutionary or
iterative methods were used. The later the requirements
in the cycle of development change, the more costly is
that revision (Boehm & Pappacio [11]). It is certainly the
case as Hoorn et al. [12] report that owing to many shifts
in focus and priorities, stakeholders become inconsistent
about what they actually want to accomplish with the
system. If we are to improve the requirements process
then proper models of a process are needed. Kotanya &
A Review of the Impact of Requirements on Software Project Development Using a Control Theoretic Model
Copyright © 2010 SciRes. JSEA
Sommerville [13] outlines the requirements engineering
process as shown in Figure 1. Although there is feed-
back between requirements validation and specification
and in the elicitation and specification as will be shown
this is not represented in the current models. It is not
clear in any of the texts on the subject whether the in-
volvement of the use is mandated at these stages.
The whole purpose of this paper is to present simple
control system models of the project development process
including requirements, as in inventory analysis, and
demonstrate rules for stability.
2. System Dynamics
Wolstenholme [14] describes System Dynamics as:
“A rigorous method for qualitative description, explo-
ration and analysis of complex systems in terms of their
processes, information, organizational structure and
strategies; which facilitates simulation modelling and
quantitive analysis for the design of system structure and
This definition is expanded in Table 1 taken from
The SD model structure is highly non-linear with a
number of theoretical assumptions, for example about
how the errors in the coding are propagated.
These structural assumptions do not allow for System
Dynamics models to enable any general rules to be de-
veloped by academics for managers to make sound
judgments based on good analysis. The distinction with
models of inventory processes, which are related, is the
rationale for this research program. Early SD invent-
tory models developed by Forrester [15] were also
non-linear and contained a number of factors, such as
employment rate, that made the problem too complex for
simple rules to be developed.
The simplest expression of representation of require-
ments in SD models is that use by Madachy [16], shown
in Figure 2. In this case requirements are added to by a
rate of generation, usually constant. The time taken to
acquire the whole requirements is dictated by the acqui-
sition rate. Häberlein [17] proposed a different structure
for the development of the whole project. In his model
(Figure 3) the rate of generation of requirements is split
into several phases depending on the comprehension of
the supplier and how this is influenced. This model could
show considerable promise but no equations are pre-
sented. The model of Williams [18] (Figure 4) could not
be evaluated further at this time due to incomplete equa-
tions. The structure indicated shows dependence on
quantities such as customer satisfaction that are not read-
ily measured during the process. The model of Anders-
son and Karlsson [19] (Figure 5) is the most complete
and useful model out in the literature. Not only are all the
equations given, with data, but the results are of a project
in industry. This model shows that the process of gaining
requirements is split into a phase where the level of re-
quirements tasks to be completed is gained via an input
pulse function. The required tasks to be completed are
fed from the previous state by a constant requirements
completion rate. Rework is discovered in these require-
ments and this is fed back at a constant rate to the first
level. Inadequate requirements are discarded at a rate that
Figure 1. Requirements engineering (from Kotanya & Sommerville).
A Review of the Impact of Requirements on Software Project Development Using a Control Theoretic Model
Copyright © 2010 SciRes. JSEA
Table 1. System Dynamics a subject summary from Wolstenholme [14].
Qualitative system dynamics Quantitative system dynamics
(diagram construction and analysis phase) (Simulation phase)
Stage 1 Stage 2
1. of existing/proposed systems 1. To examine the behavior of all system
variables over time.
2. To create and examine feedback loop
3. To provide a qualitative assessment of the
relationship between system process struc-
ture, information structure, delays organiza-
tional structure and strategy
2. To examine the validity and sensitivity of
the model to changes in
Information structure
Delays and uncertainties
1. To examine alternative system structures and
control strategies based on
Intuitive ideas
Control theory analogies
Control theory algorithms: in terms of
non-optimizing robust policy design
Figure 2. Raymond Madachy’s model.
Figure 3. Requirements as a total process in comparison to
Abdel-Hamids’ task based mod.
Figure 4. Requirements model of Williams [17].
is also a constant’. The final finished requirements are
fed by a finished requirements rate. A number of
non-linear “constants” are embedded into the system. No
proper validation is made of this model or any of the
models given here (this is normally very difficult).
A Review of the Impact of Requirements on Software Project Development Using a Control Theoretic Model
Copyright © 2010 SciRes. JSEA
Figure 5. The model of Andersson and Karlsson [18].
Do any or all of these models match the published
material on requirements engineering? In the broadest
sense, yes, they do match what is contained in books
such as Sommerville. To make further progress let us
assume that the Anderson and Karlsson model is correct.
This non-linear SD model has been linearised and ana-
lysed using control theory to see any general lessons can
be learned.
3. Control Analysis
Part of the simplification of the Project Model is being
tackled in the USA by the newer control system models
of software testing (Cangussu et al. [20]) and the ap-
proach to control of software development by White
In this case the model of Andersson and Karlsson was
linearized and the following state equations obtained:
drttbc crr rcr rw
dt 
drtc rcrfrrrw irr
dt 
dir irr
dt (3)
dt (4)
The linearized auxiliary SD equations are:
crrfi t
(where this is a pulse of height fi, the initial estimate of
the number of requirements).
irr rtc
 (7)
rwrwp rtc (8)
rrrwp rtc
 
These equations can be represented by a state-space
 (10)
where A, B and B' are given by:
01 00
01 00
rtt rtt
rwp rwp
rp rp
rwp rp
  
A (11)
B (12)
B (13)
A Review of the Impact of Requirements on Software Project Development Using a Control Theoretic Model
Copyright © 2010 SciRes. JSEA
0001C (14)
x (16)
where u = pulse function and v = rprod. In this configu-
ration v acts as a disturbance.
State-space theory can be used to see if this system is
either controllable or observable.
We can define two matrices that will allow a measure
of these properties if they are both full rank. The control
stability is defined by the four eigenvalues two zero and
two damped complex conjugates. The system is neutrally
stable at best.
Cm=B AB AB AB (17)
The rank of Cm is 1! The observability is given by:
Om =CA
The rank of this matrix is also 1. This means that the
system described by the linearized state equations is un-
controllable and unobservable! The principle reason for
this is that no corrective forces exist to alter the rate of
production of requirements and that the rework and in-
adequate requirements cannot be altered independently
of each other. Although a set of parameters will allow the
requirements to be produced, once set in train no process
exists to vary that process. No variation in workforce for
example is set up in this model. No simple solutions
allow this model to be put into a controllable form, al-
though it can be made observable.
4. Conclusions
All the SD models illustrated here would appear to use a
constant rate of conversion of requirement wishes from
the customer to specifications, depending strictly on staff
productivity. The number of staff in the cases cited
appears to be fixed at the start of the process and altered
only reluctantly, taking no account of project size or
complexity. If this is generally true it has severe implica-
tions for the later analysis and development of the project.
The most comprehensive model cited, due to Andersson
and Karlsson has been analysed from a control system
viewpoint. This analysis shows that such models are
neutrally stable since there are no feedback mechanisms
to establish when all the requirements are obtained, and
they are neither controllable nor observable. The problem
is that only the group of states fr, ir and rtc together are
specified, one of them cannot be separately described or
made to achieve a particular trajectory If the staff pro-
ductivity is fixed and the number of staff is decided be-
forehand then the final outcome is proscribed. They can
with some manipulation be made stabilizable.
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Crr Customer requirements rate
fi Initial value of requirements assumed
fr finished requirements
frr finished requirements rate
ir Inadequate requirements
irr inadequate requirements rate
rtc Requirement Tasks Completed
rcr requirements completed rate
rp requirement part
rprod requirement productivity
rtt fraction of tasks inadequate
rttbc Requirement tasks to be completed
Rw rework rate
Rwp rework fraction of RTC