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.
Document type :
Conference papers
Complete list of metadatas

Cited literature [9 references]  Display  Hide  Download

https://hal.inria.fr/hal-01060634
Contributor : Hal Ifip <>
Submitted on : Thursday, November 16, 2017 - 3:37:32 PM
Last modification on : Sunday, December 17, 2017 - 1:11:24 AM
Long-term archiving on : Saturday, February 17, 2018 - 2:04:51 PM

File

HongC10.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Lina Hong, Rong Chen. Statistical Fault Localization with Reduced Program Runs. 6th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI), Oct 2010, Larnaca, Cyprus. pp.319-327, ⟨10.1007/978-3-642-16239-8_42⟩. ⟨hal-01060634⟩

Share

Metrics

Record views

516

Files downloads

84