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Using Complexity Measures to Evaluate Software Development Projects: A Nonparametric Approach

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  • Qing Cao
  • Vicky Gu
  • Mark Thompson

Abstract

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.

Suggested Citation

  • Qing Cao & Vicky Gu & Mark Thompson, 2012. "Using Complexity Measures to Evaluate Software Development Projects: A Nonparametric Approach," The Engineering Economist, Taylor & Francis Journals, vol. 57(4), pages 274-283.
    • Handle: RePEc:taf:uteexx:v:57:y:2012:i:4:p:274-283
    DOI: 10.1080/0013791X.2012.729878
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