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Using Data Envelopment Analysis in software development productivity measurement.

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Title: Using Data Envelopment Analysis in software development productivity measurement.
Authors: Asmild, Mette1, Paradi, Joseph C.2 paradi@mie.utoronto.ca, Kulkarni, Atin2
Source: Software Process: Improvement & Practice. Nov2006, Vol. 11 Issue 6, p561-572. 12p. 1 Diagram, 5 Charts.
Subjects: Software engineering management, Computer software development, Financial institution software, Project evaluation, Project management, Data envelopment analysis, Regression analysis
Abstract: The ever-increasing size and complexity of software systems make the cost of developing and maintaining software important. Unfortunately, the process of software production has not been particularly well understood. This article helps clarify the relationship between postimplementation function points (FP) and the corresponding development effort for software development projects in a large Canadian bank. Knowledge of this relationship enables evaluations of the productivity of completed projects and, in particular, provides a predictive tool for future projects. The empirical analysis employs a combination of traditional regression models and Data Envelopment Analysis (DEA). The regression analyses show a log-linear relationship between project size and development effort, which is subsequently used in the DEA models. The DEA models identify best performers and use these as benchmarks, but are not limited to the constant returns to scale assumption of the regression analyses and are capable of including the delivery time as a nondiscretionary input. Finally, by including data from the International Software Benchmarking Standards Group (ISBSG) repository in the DEA models, the bank's projects are benchmarked not only against its own best performers but also against what is globally feasible. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:The ever-increasing size and complexity of software systems make the cost of developing and maintaining software important. Unfortunately, the process of software production has not been particularly well understood. This article helps clarify the relationship between postimplementation function points (FP) and the corresponding development effort for software development projects in a large Canadian bank. Knowledge of this relationship enables evaluations of the productivity of completed projects and, in particular, provides a predictive tool for future projects. The empirical analysis employs a combination of traditional regression models and Data Envelopment Analysis (DEA). The regression analyses show a log-linear relationship between project size and development effort, which is subsequently used in the DEA models. The DEA models identify best performers and use these as benchmarks, but are not limited to the constant returns to scale assumption of the regression analyses and are capable of including the delivery time as a nondiscretionary input. Finally, by including data from the International Software Benchmarking Standards Group (ISBSG) repository in the DEA models, the bank's projects are benchmarked not only against its own best performers but also against what is globally feasible. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]
ISSN:10774866
DOI:10.1002/spip.298