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Discriminative pattern mining in software fault detection

Di Fatta, G., Leue, S. and Stegantova, E. (2006) Discriminative pattern mining in software fault detection. In: 3rd International Workshop on Software Quality Assurance (SOQUA), 14th ACM Symposium on Foundations of Software Engineering, 06 Nov 1996, Portland, Oregon, USA, pp. 62-69.

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Official URL: http://doi.acm.org/10.1145/1188895.1188910

Abstract/Summary

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.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:No Reading authors. Back catalogue items
Faculty of Science > School of Systems Engineering
ID Code:6137
Uncontrolled Keywords:automated debugging, fault isolation
Publisher Statement:Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. SOQUA ’06, November 6, 2006, Portland, OR, USA. Copyright 2006 ACM 1-59593-584-3/06/0011 ...$5.00.

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