Statistical Fault Localization with Reduced Program Runs

Abstract : A typical approach to software fault location is to pinpoint buggy statements by comparing the failing program runs with some successful runs. Most of the research works in this line require a large amount of failing runs and successful runs. Those required execution data inevitably contain a large number of redundant or noisy execution paths, and thus leads to a lower efficiency and accuracy of pinpointing. In this paper, we present an improved fault localization method by statistical analysis of difference between reduced program runs. To do so, we first use a clustering method to eliminate the redundancy in execution paths, next calculate the statistics of difference between the reduced failing runs and successful runs, and then rank the buggy statements in a generated bug report. The experimental results show that our algorithm works many times faster than Wang's, and performs better than competitors in terms of accuracy.
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Communication dans un congrès
Harris Papadopoulos; Andreas S. Andreou; Max Bramer. 6th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI), Oct 2010, Larnaca, Cyprus. Springer, IFIP Advances in Information and Communication Technology, AICT-339, pp.319-327, 2010, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-16239-8_42〉
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Lina Hong, Rong Chen. Statistical Fault Localization with Reduced Program Runs. Harris Papadopoulos; Andreas S. Andreou; Max Bramer. 6th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI), Oct 2010, Larnaca, Cyprus. Springer, IFIP Advances in Information and Communication Technology, AICT-339, pp.319-327, 2010, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-16239-8_42〉. 〈hal-01060634〉

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