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25th International Conference on Software Engineering (ICSE'03)
Automated Support for Classifying Software Failure Reports
Portland, Oregon
May 03-May 10
ISBN: 0-7695-1877-X
Andy Podgurski, Case Western Reserve University
David Leon, Case Western Reserve University
Patrick Francis, Case Western Reserve University
Wes Masri, Case Western Reserve University
Melinda Minch, Case Western Reserve University
Jiayang Sun, Case Western Reserve University
Bin Wang, Case Western Reserve University
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.
Citation:
Andy Podgurski, David Leon, Patrick Francis, Wes Masri, Melinda Minch, Jiayang Sun, Bin Wang, "Automated Support for Classifying Software Failure Reports," icse, pp.465, 25th International Conference on Software Engineering (ICSE'03), 2003
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