Discriminative pattern mining in software fault detection

Authors:
Giuseppe Di Fatta;Stefan Leue;Evghenia Stegantova
Affiliations:
University of Konstanz, Konstanz, Germany;University of Konstanz, Konstanz, Germany;University of Konstanz, Konstanz, Germany
Venue:
Proceedings of the 3rd international workshop on Software quality assurance
Year:
2006

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Abstract

We present a method to enhance fault localization for software systems based on a frequent pattern mining algorithm. Our method is based on a large set of test cases for a given set of programs in which faults can be detected. The test executions are recorded as function call trees. Based on test oracles the tests can be classified into successful and failing tests. A frequent pattern mining algorithm is used to identify frequent subtrees in successful and failing test executions. This information is used to rank functions according to their likelihood of containing a fault. The ranking suggests an order in which to examine the functions during fault analysis. We validate our approach experimentally using a subset of Siemens benchmark programs.