By no means: a study on aggregating software metrics

  • Authors:

  • Bogdan Vasilescu;Alexander Serebrenik;Mark van den Brand

  • Affiliations:

  • Technische Universiteit Eindhoven, Eindhoven, Netherlands;Technische Universiteit Eindhoven, Eindhoven, Netherlands;Technische Universiteit Eindhoven, Eindhoven, Netherlands

  • Venue:
  • Proceedings of the 2nd International Workshop on Emerging Trends in Software Metrics
  • Year:
  • 2011

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Abstract

Fault prediction models usually employ software metrics which were previously shown to be a strong predictor for defects, e.g., SLOC. However, metrics are usually defined on a microlevel (method, class, package), and should therefore be aggregated in order to provide insights in the evolution at the macro-level (system). In addition to traditional aggregation techniques such as the mean, median, or sum, recently econometric aggregation techniques, such as the Gini, Theil, and Hoover indices have been proposed. In this paper we wish to understand whether the aggregation technique influences the presence and strength of the relation between SLOC and defects. Our results indicate that correlation is not strong, and is influenced by the aggregation technique.