Common Factors Influencing Software Project Effort

Abstract

The effort required for developing software depends on a number of factors. The major determinant of development effort is the “size” of software, typically approximated in terms of the amount of functionality delivered by the software or the structural size of artifacts delivered by software engineering processes. Still, developing software of comparable size may require significantly different amounts of effort. This variance is related to differences in development productivity, which in turn depends on the environment of the software development project. The ability to reliably estimate software size, knowledge of the most relevant factors influencing development productivity, and an understanding of how they influence productivity are the three pillars of successful prediction of software development effort. The topic of measuring software size is so extensive that it deserves its own book (at least one) and thus is beyond the scope of this work; in Appendix A, we briefly present the most common ways of measuring software size with their most prominent strengths and weaknesses. In this chapter, we discuss common factors that determine software development productivity and thus should be considered when estimating software project effort.

Further Reading

• A. Trendowicz and J. Münch (2009), “Factors Influencing Software Development Productivity - State of the Art and Industrial Experiences.” Advances in Computers, vol. 77, Elsevier Science Inc., pp. 185–241.

  This article provides a comprehensive overview of the most common context factors and effort drivers. The authors discuss effort drivers that seem to be universally applicable across various project environments and those that seem to apply only within particular project situations.

• B. Kitchenham (1992), “Empirical Studies of the Assumptions That Underline Software Cost-Estimation Models”, Information and Software Technology, vol. 34 no. 4, pp. 211–218.

  The author examines different software engineering data sets with respect to the existence of economies of scale. The investigation does not provide statistical support for any economies and diseconomies of scale effect.

• B. Kitchenham (2002), “The question of scale economies in software - why cannot researchers agree?” Information and Software Technology, vol. 44, no. 1, pp. 13–24.

  The author summarizes results of investigations performed by many researchers regarding the existence of (dis)economies of scale in the software engineering context. Possible sources of significant disagreement on (dis)economies effect in the software community are discussed from the perspective of the potential threats to the validity of empirical investigations that underline contradicting conclusions.

• J. Persson and L. Mathiassen (2010), “A Process for Managing Risks in Distributed Teams,” IEEE Software, vol. 27, no. 1, pp. 20–29.

  A process for managing risks related to distributed software development teams is defined. They also provide an overview of typical risk factors, and for each factor they describe situations that would result in low, medium, and high risk, respectively. Finally, they propose appropriate risk resolution techniques and guidelines and how to compose them into appropriate risk mitigation plans.

• C. Sauer, A. Gemino, and B.H. Reich (2007), “The impact of size and volatility on IT project performance,” Communications of ACM, vol. 50, no. 11, pp. 79–84.

  The authors discuss the impact of the size and volatility of a software project on its performance. They investigate several factors that contribute to reduction in project performance as its scale increases.

• J.P. Scott (2000), Social Network Analysis: A handbook. 2 ^nd Edition. Sage Publications Ltd.

  This book discusses the characteristics of team work in terms of social relationships among team members. In social network analysis, these relationships are realized as a network. A set of principle metrics is proposed to quantitatively analyze properties of a social network. Aspects such as network density, centrality, or cohesion are considered as useful indicators of potential deficits in team performance.

• Z. Bachore and L. Zhou (2009), “A Critical Review of the Role of User Participation in IS Success,” Proceedings of the 15th Americas Conference on Information Systems, San Francisco, California, USA.

  The authors provide a comprehensive review of the studies that investigate the influence of user participation on the success of a software development project.

• J. Shim, T.S. Sheu, J.J. Jiang, and G. Klein (2010), “Coproduction in the Successful Software Development Project,” Information and Software Technology, vol. 52, No. 10, pp. 1062–1068.

  An investigation of key factors for the successful involvement of users in software development projects. They consider “partnership” between developers and users as a crucial driver supporting the expertise exchange. Authors list a number of detailed aspects that need to be considered when assessing the level of partnership between customers and developers as well as when locating and applying expertise required in the project.

• A. Trendowicz, A. Wickankamp, M. Ochs, J. Münch, Y. Ishigai, and T. Kawaguchi (2008), “Integrating Human Judgment and Data Analysis to Identify Factors Influencing software Development Productivity”, e-Informatica Software Engineering Journal, vol. 2, no. 1, pp. 47–69.

  This article discusses alternative strategies for selecting relevant effort drivers. The authors discuss the strengths and weaknesses of approaches based on human judgment and data analysis. They propose a novel, hybrid approach that combines both strategies in order to exploit their strengths and to avoid their weaknesses. The method elicits relevant factors by integrating data analysis and expert judgment approaches by means of a multicriteria decision analysis (MCDA) technique. Empirical evaluation of the method in an industrial context indicates that it delivers a different set of factors compared to individual data- and expert-based factor selection methods. Moreover, application of the integrated method significantly improves the performance of effort estimation in terms of accuracy and precision.