Using data envelopment analysis in software development productivity measurement
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Using data envelopment analysis in software development productivity measurement. / Asmild, Mette; Paradi, Joseph C.; Kulkarni, Atin.
In: Software Process Improvement and Practice, Vol. 11, No. 6, 2006, p. 561-572.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Using data envelopment analysis in software development productivity measurement
AU - Asmild, Mette
AU - Paradi, Joseph C.
AU - Kulkarni, Atin
PY - 2006
Y1 - 2006
N2 - 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.
AB - 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.
KW - Bank
KW - Data envelopment analysis (DEA)
KW - Development effort
KW - Function points
KW - Productivity
KW - Software development
U2 - 10.1002/spip.298
DO - 10.1002/spip.298
M3 - Journal article
AN - SCOPUS:33845952862
VL - 11
SP - 561
EP - 572
JO - Software Process Improvement and Practice
JF - Software Process Improvement and Practice
SN - 1077-4866
IS - 6
ER -
ID: 227787749