Assessing and Understanding Efficiency and Success of SoftwareProduction

  • Authors:

  • A. Von Mayrhauser;C. Wohlin;M. C. Ohlsson

  • Affiliations:

  • Computer Science Department, Colorado State University, Fort Collins, CO 80523, USA;Dept. of Communication Systems, Lund University, Box 118, SE-221 00 Lund, Sweden;Dept. of Communication Systems, Lund University, Box 118, SE-221 00 Lund, Sweden

  • Venue:
  • Empirical Software Engineering
  • Year:
  • 2000

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Abstract

One of the goals of collectingproject data during software development and evolution is toassess how well the project did and what should be done to improvein the future. With the wide range of data often collected andthe many complicated relationships between them, this is notalways easy. This paper suggests to use production models (DataEnvelope Analysis) to analyze objective variables and their impacton efficiency. To understand the effect of subjective variables,it is suggested to apply principal component analysis (PCA).Further, we propose to combine the results from the productionmodels and the analysis of the subjective variables. We showcapabilities of production models and illustrate how productionmodels can be combined with other approaches to allow for assessingand hence understanding software project data. The approach isillustrated on a data set consisting of 46 software projectsfrom the NASA-SEL database (NASA-SEL, 1992). The data analyzedis of the type that is commonly found in project databases.