Automated support for classifying software failure reports

  • Authors:

  • Andy Podgurski;David Leon;Patrick Francis;Wes Masri;Melinda Minch;Jiayang Sun;Bin Wang

  • Affiliations:

  • Case Western Reserve University, Cleveland, OH;Case Western Reserve University, Cleveland, OH;Case Western Reserve University, Cleveland, OH;Case Western Reserve University, Cleveland, OH;Case Western Reserve University, Cleveland, OH;Case Western Reserve University, Cleveland, OH;Case Western Reserve University, Cleveland, OH

  • Venue:
  • Proceedings of the 25th International Conference on Software Engineering
  • Year:
  • 2003

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Abstract

This paper proposes automated support for classifying reported software failures in order to facilitate prioritizing them and diagnosing their causes. A classification strategy is presented that involves the use of supervised and unsupervised pattern classification and multivariate visualization. These techniques are applied to profiles of failed executions in order to group together failures with the same or similar causes. The resulting classification is then used to assess the frequency and severity of failures caused by particular defects and to help diagnose those defects. The results of applying the proposed classification strategy to failures of three large subject programs are reported. These results indicate that the strategy can be effective.