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ICSE 2003

Automated Support for Classifying Software Failure Reports

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

DOI 10.1109/icse.2003.1201224dblp

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.

BibTeX
@inproceedings{Podgurski-al:ICSE03,
  author    = {Andy Podgurski and
               David Leon and
               Patrick Francis and
               Wes Masri and
               Melinda Minch and
               Jiayang Sun and
               Bin Wang},
  title     = {Automated Support for Classifying Software Failure Reports},
  booktitle = {ICSE},
  pages     = {465--477},
  publisher = {{IEEE} Computer Society},
  year      = {2003},
}

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