Factors Affecting the Choice of Software Life Cycle Models in the Software Industry-An Empirical Study

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Factors Affecting the Choice of Software Life Cycle Models in the Software Industry-An Empirical Study

Factors Affecting the Choice of Software Life Cycle Models in the Software Industry-An Empirical Study

1Rupa Mahanti, 2M.S. Neogi and 3Vandana Bhattacherjee

1Department of Computer Consultancy, Tata Consultancy Services, USA

2Department of I and M, Xavier Institute of Social Service Ranchi, India

3Department of CSE, Birla Institute of Technology, Ranchi, India

Abstract: Problem statement: The aim of this study was to present the results of the survey conducted with software professionals in a few Indian software companies. Approach: The study initially presents an overview of the common software life cycle models used in the software development. Results and Conclusion: The survey results revealed that the level of understanding of the user requirements is the most important fact in the choice of the life cycle model used in the software project. Project Complexity is the second most important factor. Man-machine Interaction is the least important factor in the choice of the life cycle model used in the software project. This study will be valuable for developers, analysts and project leaders in software organizations. This study was carried out with some boundaries like the number of companies, available resources, time constraints and so on.


Key words: Incremental model, cycle process, important factor, agile methodologies, software project, employee strength, software development, software life cycle models





Large software systems, developed over several years, are the backbone of industries such as banking, retail, transportation, defense, healthcare and telecommunications. In other words, software has become an integral part of our life.

Developing software, which is within cost and time

schedule, fulfils customer requirements and is reliable, seems to be the ultimate challenge for today’s software professionals and  calls  for  a  systematic approach to software development. Once upon a time, software development consisted of a programmer writing code to solve a problem or automate a procedure. In those days, whenever a developer was tasked to perform programming or coding, he immediately would jump to it, start programming with or without full knowledge of what the system would look like, how the features were arranged. This was feasible because systems were very simple. However, nowadays, systems are so big and complex  that  teams  of  architects,  analysts, programmers, testers and users must work together to create the millions of lines of custom-written code that drive  our  enterprises.  In  the  absence  of  a  proper software development plan,  the  developer  is  full  of ideas, he/she wants to implement them all, but tends to forget about them because other features need to be prioritized. To manage this, a number of System Development Life Cycle (SDLC) models have been created.  The  advantage  of  adhering  to  a  life  cycle

model is that it follows a systematic and discipline manner. It saves time, features of the system are well documented  and  above  all,  there  is  proper management and execution of plans. Without a life cycle  model  in  place,  the  probability of  chaos  and project failure would have been very high (Russell,

2002; Ghezzi et al., 2002).

A lot of research has been reported on the evolution of software life cycle models. Agile methodologies are emerging and gaining popularity in industry (Manhart and Schneider, 2004; Cockburn and Highsmith, 2001; Coram and Bohner, 2005; Huo et al., 2004; Boehm,

2002; Highsmith, 2002, Kadary et al., 1989; Konito et

al., 2004). Research has been reported regarding the suitability of different life cycle models and comparison of different software life cycle models (Davis et al.,

1988).   Some   research   has   been   reported   on   the

relationship between project categories and life cycle models (Archibald and Vladmir, 2003; 2004; Archibald et  al.,  2003;  Archibald,  2004b;  Desaulniers  et  al.,

2001). However, no empirical study has been reported regarding the importance of the factors affecting the choice of software life cycle models in the software industry. This study begins with an overview of the common  software  life   cycle   models  used   in   the software development. This is followed by the research methodology. A survey was conducted in the Indian software organizations to study the factors affecting the


Corresponding Author: Rupa Mahanti, Computer Consultancy, Tata Consultancy Services, USA




choice of software life cycle models in the Indian software industry. This is followed by the presentation of the results the survey.

However,  there  is  no  universal  life  cycle  model,

which is considered adequate in all situations in the development environment . Plan driven approaches like the waterfall model assume that requirements are static. Other iterative methods like spiral model and evolutionary model count on change. Agile methodologies consider software development as an empirical process and that

people play the most important role in it. It has also been

observed that agile practices do not compromise the quality of software products (Huo et al., 2004). A detailed description of traditional and agile methodologies can be found in literature (Brooks, 1995; McConnell, 1996; Szyperski, 1998; Pressman, 2004; Ghezzi et al., 2002; Jalote, 2005; Beck, 2000; Cockburn, 2001; Jeffries et al.,

2000; Martin, 1991; Salo, 2004; Siponen et al., 2005; Scacchi, 2002; Neogi et al., 2007).


Overview of software life cycle models: The fundamental principle of software engineering is to design software products that minimize the intellectual distance between the problem and solution. Today methodical approaches to software design have evolved and design notations have proliferated. Many steps are involved in the successful development and deployment of computer software. Taken together, all these steps are referred to as the software life cycle.

From the IEEE Standard Glossary of Software Engineering Terminology, 1983, software life cycle is defined as follows.

‘The period of time that starts when a  software product is conceived and ends when the product is no longer available for use. The software life-cycle typically includes a requirements phase, design phase, implementation   phase,   test   phase,   installation   and checkout phase,  operation and  maintenance phase  and sometimes, retirement phase’

The purposes of designing and documenting the

overall project life cycle process for each project category are to:


Enable all persons concerned with creating, planning and executing projects to understand the process to be followed during the life of the project Capture the best experience within the organization so that the life cycle process can be

improved  continually  and  duplicated  on  future


Enable all the project roles and responsibilities and the   project   planning,   estimating,   scheduling,

monitoring and control methods and tools, to be appropriately  related  to  the  overall  project  life cycle management process (Archibald, 2003; Archibald, 2004a)


Life cycle models for software development provide the basic guidelines for developing software using engineering technique. The first task of a software life cycle model is to determine the sequence of stages in software development and evolution and to establish the transition criteria for progression from one stage to the next. There are several life cycle models and many companies adopt their own models, but all have very similar patterns. Different life cycle models are the Code and Fix model, Classical Waterfall model, Iterative Waterfall model, Incremental model, Throwaway Prototyping Model, Evolutionary Prototyping model, Spiral model, Agile model, Extreme programming:


Code  and  Fix  Model:  In  the  beginning  of  the software  era,  software  process  models  included simply  writing  some  code  and  trying  to  fix  the problem. This is called code and fix model. It is a two-phase model. The first phase is to write the code and next phase is to fix it (Connell et al., 1993) Classical Waterfall Model: The Classical Waterfall Model   was   popularized   in   1970   and   is   the backbone   of   many   other   software   life   cycle models.  This  process  model  is  structured  as  a cascade of phases, where output of one phase acts as  the  input  to  the  next  phase.  The  classical waterfall model is an unrealistic one since there is no provision of detecting and rectifying the error at any stage of the life cycle. However in practical developments, there is always chance of errors, due to various reasons, in almost every phase of the life cycle.    Therefore    in    any    practical    software development  work,  it  is  not  possible  to  strictly follow the classical waterfall model (Royce, 1987) Iterative Waterfall Model: Iterative waterfall model suggests feedback paths in the classical waterfall model from every phase to its preceding phases. It   allow   for   the   correction   of   the   errors committed during a  phase that  are  detected in later  phases.  After  detecting  the  error  in  later phases, it would be necessary not only to rework the  design,  but  also  to  appropriately  redo  the coding  and  the  system  testing,  thus  incurring higher cost (Ghezzi et al., 2002)

V-Shaped Model: Like the waterfall model, the V- Shaped model is sequential path of execution of processes i.e., linear in nature. Each phase must be completed before the next phase starts. However,




emphasis on testing in this model is more than that in the waterfall model (Raymond, 2005) Throwaway  prototyping model: It was advocated by  Brooks.  It  is  useful  in  in  situations  where requirements and user’s needs are unclear or poorly specified. The approach is to construct a quick and dirty partial implementation of the system during

or before the requirements phase (Brooks, 1995; Gomma and Scott, 1981; Jalote, 2005)

Evolutionary prototyping model: This is  kind of mix     of Waterfall     model     and     prototyping. Presuppose  gradual  refinement  of  the  prototype until a usable product emerges. Might be suitable in   projects  where   the   main   problem  is   user interface requirements, but internal architecture is relatively well established and static (Jalote, 2005) Rapid  Application  Development  (RAD):  Rapid Application Development model was proposed in

1980 by IBM. This model is based on an evolving prototype that is not thrown away. . Rapid Application Development model is the first model, which emphasizes a short development cycle e.g.,

60 to 90 days. It is a “high-speed” adaptation of the waterfall  model,  in  which  rapid  development is achieved by using component based construction approach (Butler, 1994; Martin, 1991)

Unified Process Model: During late 1980’s and early 1990’s, James Rumbaugh, Grady Booch and Ivar Jacobson developed the Unified Process, a framework, which is “use-case driven, architecture-centric, iterative and incremental” (Jacobson et al., 1999). The Unified Process Model consists of five phases

Inception phase incorporates both customer communication and planning activities and emphasizes on refinement and development of use- cases as primary model

Elaboration phase consist of customer communication and design activity

Construction phase produces an implementation model  that   translates  design  classes  produced during elaboration phase into software components that will be built to realize the system.

Transition phase transfers the software from the developer to the end-user for beta testing and acceptance.

Production   phase   in   which   on-going   use   of software is monitored and infrastructure support is provided (Jacobson et al., 1999)

Incremental Model: It is decomposition of a large development effort  into  a  succession of  smaller

components. The life cycle is also referred to as the successive versions or evolutionary model. Incremental model is an intuitive approach to the waterfall model. Multiple development cycles take place   here,   making   the   life   cycle   a   “multi- waterfall”   cycle.   Cycles   are   divided   up   into smaller, more easily managed iterations. Each iteration passes through the requirements, design, implementation and testing phases. A working version of  software is  produced during the  first iteration, so you have working software early on during the  software  life  cycle.  Subsequent iterations build  on  the  initial  software  produced during the first iteration (Jalote, 2005; Pressman,

2004; McDermid, 1993)

Spiral Model: Boehm proposed the Spiral model in

1988 (Boehm, 1988). It involves repetition of the same set of life-cycle phases such as plan, develop, build and evaluate until development is complete. The main emphasis is given on risk analysis. It encounters almost all the different types of risks such as cost overruns, change in requirements, loss

of  intelligent project personnel, unavailability of

necessary hardware, competition from other software developers, technological drawbacks which obsolete the project and many more (Boehm et al., 1998; Boehm et al., 2000; Boehm and Hansen, 2001)

Agile Software Development: In 2001, Kent Beck and 16 other noted software developers proposed an  agile  view of  process.  Agile  software engineering combines a philosophy and a set of development  guidelines.  The  philosophy encourages customer satisfaction and early incremental delivery of software, small, highly motivated  project  teams,  informal  methods, minimal software engineering work products and overall development simplicity. The development guidelines stress on delivery over analysis and design and active and continuous communication between developers and customers (Beck, 2000). The term ‘agile’ refers to a philosophy of software development. Extreme Programming, Scrum, Crystal,  Adaptive  Software  Development  (ASD) are agile methodologies (Boehm, 2002; Cockburn,

2001; Highsmith, 2002)


Research methodology: Research methodology can be viewed  as  the  process  taken  to  accomplish the  key objectives of the research undertaken. The objectives of this research project were:




To study the awareness/importance of software life cycle models in the Indian software Industry

To identify the factors affecting the choice of the software life cycle models in the software/IT industry

To determine the importance of factors affecting the choice of the software life cycle models


Authors have undertaken a survey-based approach to assess use software life cycle models in Indian Software Industry. In a survey based approach the usual proceeding to gather information is the usage of questionnaires or  interviews.  These  are  applied  to  a representative sample group and the outcomes are then analyzed. The  aim  is  to  derive  conclusions that  are descriptive, exploratory or explanatory. With the use of generalization the result from the sample is mapped to the whole group. It is, however, not possible to manipulate or control the samples. Nevertheless it is practicable to compare the result with similar outcomes of other surveys. Both qualitative as well as quantitative data can be derived from this strategy. Which one it is depends on the data that is being collected through the questionnaires or interviews and whether statistical analysis methods are applicable or not. Questionnaire survey methodology was preferred for this research since it is a reliable and economical method for data collection. In addition to the questionnaires, telephonic interviews were conducted to understand the relation between the factors affecting the choice of software life cycle models and the each individual software life cycle model.

Questionnaire survey methodology was preferred for this research since it is a reliable and economical method for data collection. An email survey was used to gather survey data. The advantages of the email survey approach to data collection are (Neuman, 2003; Sarantakos, 1998):



Results are produced quickly

Questionnaires are completed in the respondents’


Anonymity is greatly assured; and

Respondents  are  at  liberty  to  provide  objective views on sensitive issues,


The questionnaire used in this study consisted of three parts:


The software personnel information The background of the company The software process information

 Table 1: Survey questionnaire                                                     

 Notation                       Factors                                                     F1                                  Nature/type of project

F2                                 Project size

F3                                 Project duration

F4                                 Project complexity

F5                                 Level and type of expected risk

F6                                 Level of understanding of user requirements F7                                 Level of understanding of the application area F8                                 Customer involvement

F9                                 Experience of developers

F10                               Team size

F11                               Man-machine interaction

F12                               Availability of tools and technology

F13                               Versions of the product

F14                               Level of reliability required


The  first  part  dealt  with  the  software personnel information such as experience and his/her designation. The  second  part  was  primarily aimed  to  understand some of the fundamental issues such as the size of the company and service areas. Third part of the questionnaire dealt with understanding the type of projects, processes and life cycle models. 14 factors affecting the choice of software life cycle models were derived mainly from the literature (Pressman, 2004; Ghezzi et al., 2002; Jalote, 2005; Martin, 1991; Archibald and Vladmir, 2004) and discussions with software quality professionals as shown in Table 1 below.

All factors were ranked on a five-point scale (1 =

not very important, 2 = not important, 3 = important, 4

= very important and 5 = critical). The list of companies was obtained from National Association of Software

and Services Companies (NASSCOM) database as well

as  using  search  engines  (www.google.com).  In  this study, a total of 100 questionnaires were sent by email

to  software  companies.  The  response  rate  from  the

companies was 51% (i.e., 51companies).

Results of  the empirical investigation: The service areas of the companies participating in the survey comprised of Internet, software consultancy and services, data warehousing, IT enabled services, data mining, embedded technology, training and education, advanced databases, software vendor, telecommunication, ERP, mainframe technology, engineering design services and  transportation sector services.  60%  of  the  companies  participating in  the survey had multiple service areas. The rest 40% had only one service area. 50% of the companies had software  consultancy  and  services  as  one  of  their service   area.   As   shown  in   Fig.   1,   52%   of   the participants of the Software Development Life Cycle model survey were big companies with more than 1000 employees; 18% of the respondent hailed from companies with employee strength between 501  and

1000; 12% of the respondents were companies with

301-500  employees.  12%  of  the  respondents  were




companies with 301-500 employees. The remaining 6%

of the companies had employee strength of less than

  1. 100. Figure 2 shows the total work experience of the participants of survey. Figure 3 shows the designations

of the individuals participating in the survey. All the respondents had worked in more than multiple projects

in different technologies and business domains in the


software industry. 50% of the respondents had worked on Commercial software projects; 30% had worked on open source software projects and web applications respectively; 25% had worked on ERP projects; 15% had worked on mission critical software projects; 10% of   the   respondents   had   worked   on   embedded software projects as shown in Table 2.

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