University of Konstanz
Graduiertenkolleg / PhD Program
Computer and Information Science

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Author(s) Di Fatta, G., Leue, S., Stegantova, E.
Title Discriminative pattern mining in software fault detection
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 classi ed 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.
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