Principles of Effort and Cost Estimation

Abstract

One of the reasons for failed estimates is an insufficient background of estimators in the area of software estimation. Arbitrary selection and the blind usage of estimation methods and tools often lead to disappointing outcomes, while the underlying reasons remain unclear. In discussions with corporate management, it is not uncommon to hear the phrase “think of a number and multiply by three.” Deliberate decisions regarding the particular estimation method and its knowledgeable use require insight into the principles of effort estimation.

Further Reading

• S. Grimstad, M. Jørgensen, and K. Moløkken-Østvold (2006), “Software Effort Estimation Terminology: The tower of Babel,” Information and Software Technology, vol. 48, no. 4, pp. 302–310.

  This article discusses the negative impact on successful project planning of using imprecise and inconsistent estimation terminology. The authors provide a structured review of typical software effort estimation terminology presented in the software engineering literature and suggests guidelines on how to avoid unclear and inconsistent terminology.

• M. Svahnberg, T. Gorschek, R. Feldt, R. Torkar, S.B. Saleem, and M.U. Shafique (2010), “A systematic review on strategic release planning models,” Information and Software Technology, vol. 52, no. 3, pp. 237–248.

  The authors provide a comprehensive review of release planning strategies proposed in the context of software engineering. This article can be used as a starting point for detailed investigation of software release planning as a key aspect of software requirements engineering and software project planning.

• Y. Wang (2007a), Software Engineering Foundations: A Software Science Perspective, CRC Software Engineering Series, vol. 2, AUERBACH/CRC Press.

  In Sect. 8.5 of his book, the author discusses the basic laws of cooperative work within software projects. The author focuses on the time-effort-staffing trade-offs of software development. He formulates several laws that formally explain empirical observations made by other researchers with respect to associations between project duration, effort, and staffing level.

• P.V. Norden (1958), “Curve Fitting for a Model of Applied Research and Development Scheduling,” IBM Journal of Research and Development, vol. 2, no. 3.

  This is the original article where Norden investigated the Rayleigh curve for modeling the distribution of software development staff across software life cycle phases.

• L.H. Putnam and W. Myers (2003), Five Core Metrics: The Intelligence Behind Successful Software Management, Dorset House, New York.

  The book proposes an effort estimation method called Software Life Cycle Management (SLIM) which is based on the Rayleigh distribution of staffing across the software development life cycle.

• K. Pillai and V.S. Sukumaran Nair (1997), “A Model for Software Development Effort and Cost Estimation”, IEEE Transactions on Software Engineering, vol. 23, no. 8, pp. 485–497.

  This article presents an adjustment of the SLIM estimation method. The distribution of staffing across the software development life cycle is represented by a Gamma curve, instead of Rayleigh.

• S.G. MacDonell and M.J. Shepperd (2007), “Comparing Local and Global Software Effort Estimation Models - Reflections on a Systematic Review,” Proceedings of the 1st International Symposium on Empirical Software Engineering and Measurement, Madrid, Spain, 20–21 September 2007, IEEE Computer Society Press, pp. 401–409.

  This article discusses utilizing company-specific and cross-company project data for building local (context-specific) and global (cross-context) effort models. The authors provide a number of references to studies where local and global effort models have been developed and validated in a particular application context.

• P. Abrahamsson, R. Moser, W. Pedrycz, A. Sillitti, and G. Succi (2007), “Effort Prediction in Iterative Software Development Processes—Incremental Versus Global Prediction Models,” Proceedings of the 1st International Symposium on Empirical Software Engineering and Measurement, Madrid, Spain, 20–21 September 2007, pp. 344–353.

  This article discusses the challenges of applying traditional effort estimation methods in the context of agile and iterative software development. The authors propose a detailed development approach and discusses a number of architectures of such estimation models, including regression models and neural networks.

• R. Stutzke (2005), Estimating Software-Intensive Systems. Projects, Products, and Processes. The SEI Series in Software engineering, Addison Wesley Professional.

  In Sect. 1.6, the author provides an overview of the estimation process, which he discusses in more detail in the remaining part of the book. Estimation activities are grouped into those performed before, during, and after projects.

• S. L. Pfleeger, F. Wu, and R. Lewis (2005), Software Cost Estimation and Sizing Methods, Issues, and Guidelines. RAND Corporation.

  A comprehensive systematic review of software sizing methods is provided including their particular benefits and threats when applied in the context of effort estimation. Moreover, a number of practical guidelines are provided for balancing the use of different sizing methods. Finally, the report provides ready-to-use checklists for considering size-estimation-related risks and for selecting the most appropriate sizing method.

• A.F. Minkiewicz (2009), “The Evolution of Software Size: A Search for Value.” CrossTalk—The Journal of Defense Software Engineering, vol. 22, no. 3, pp. 23–26.

  This article provides a review of software sizing methodologies employed over the years in the software engineering domain. The review focuses on the uses and misuses of common software size metrics.

• ISO/IEC 20926 (2003), Software engineering. IFPUG 4.1—Unadjusted functional size measurement method. Counting practices manual. International Standardization Organization.

  This international standard specifies a method for measuring the functional software size as defined by the International Function Points User Group (IFPUG) in version 4.1. Note that the ISO standard incorporates only the basic method. The complete approach, including guidelines and examples, is published by IFPUG as “Function Point Counting Practices Manual”, which can be acquired at IFPUG (http://www.ifpug.org).

• ISO/IEC 24570 (2005), Software Engineering. NESMA Functional Size Measurement Method Version 2.1. Definitions and Counting Guidelines for the Application of Function Point Analysis. International Standardization Organization.

  This international standard represents an adjustment of the IFPUG method for functional size measurement, proposed by Netherlands Software Metrics Association (NESMA). Another example of national adjustment has been proposed by the Finnish Software Measurement Association (FiSMA). Similarly to IFPUG, detailed guidelines on measuring NESMA function points are provided in associated documents published by NESMA (http://www.nesma.nl).

• ISO/IEC 19761 (2003), Software engineering. COSMIC-FFP—A Functional Size Measurement Method. International Standardization Organization.

  This standard specifies a relatively new method for measuring functional software size proposed as an alternative to the IFPUG method. It addresses the major points of criticism (weaknesses) concerning the IFPUG method, such as limited applicability in the context of embedded software systems.

• E. Mendes and C. Lokan (2008), “Replicating Studies on Cross- vs Single-company Effort Models Using the ISBSG Database,” Empirical Software Engineering, vol. 13, no. 1, Springer Netherlands, pp. 3–37.

  This article provides an overview of the studies where the ISBSG benchmark repository is used for the purpose of effort estimation. Among the other aspects, the authors discuss the differences in estimation reliability when based on organization-specific vs. multi-organizational project data.