TY - JOUR
T1 - Using complexity measures to evaluate software development projects
T2 - A nonparametric approach
AU - Cao, Qing
AU - Gu, Vicky Ching
AU - Thompson, Mark A.
PY - 2012/12/1
Y1 - 2012/12/1
N2 - In this article, we use newly developed complexity metrics for software development projects that are more useful than traditional measures such as lines of code and functional points. Next, we present an approach to assessing the relative efficiency of software projects using these complexity measures as outputs. Due to the nature of the complexity measures, the constant returns to scale assumption often used in data envelopment analysis (DEA) is not appropriate. We relax this assumption and estimate the DEA model assuming variable returns to scale. This two-step approach provides project managers with a decision support tool to assess project productivity, categorize projects, and evaluate critical success/failure factors in software development projects.
AB - In this article, we use newly developed complexity metrics for software development projects that are more useful than traditional measures such as lines of code and functional points. Next, we present an approach to assessing the relative efficiency of software projects using these complexity measures as outputs. Due to the nature of the complexity measures, the constant returns to scale assumption often used in data envelopment analysis (DEA) is not appropriate. We relax this assumption and estimate the DEA model assuming variable returns to scale. This two-step approach provides project managers with a decision support tool to assess project productivity, categorize projects, and evaluate critical success/failure factors in software development projects.
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U2 - 10.1080/0013791X.2012.729878
DO - 10.1080/0013791X.2012.729878
M3 - Article
AN - SCOPUS:84870618879
SN - 0013-791X
VL - 57
SP - 274
EP - 283
JO - Engineering Economist
JF - Engineering Economist
IS - 4
ER -