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ABSTRACT
The high cost of locating faults in programs has motivated the development of techniques that assist in fault localization by automating part of the process of searching for faults. Empirical studies that compare these techniques have reported the relative effectiveness of four existing techniques on a set of subjects. These studies compare the rankings that the techniques compute for statements in the subject programs and the effectiveness of these rankings in locating the faults. However, it is unknown how these four techniques compare with Tarantula, another existing fault-localization technique, although this technique also provides a way to rank statements in terms of their suspiciousness. Thus, we performed a study to compare the Tarantula technique with the four techniques previously compared. This paper presents our study---it overviews the Tarantula technique along with the four other techniques studied, describes our experiment, and reports and discusses the results. Our studies show that, on the same set of subjects, the Tarantula technique consistently outperforms the other four techniques in terms of effectiveness in fault localization, and is comparable in efficiency to the least expensive of the other four techniques.
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Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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CITED BY 29
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Maximilian Stoerzer , Barbara G. Ryder , Xiaoxia Ren , Frank Tip, Finding failure-inducing changes in java programs using change classification, Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering, November 05-11, 2006, Portland, Oregon, USA
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Rui Abreu , Alberto González , Peter Zoeteweij , Arjan J. C. van Gemund, Automatic software fault localization using generic program invariants, Proceedings of the 2008 ACM symposium on Applied computing, March 16-20, 2008, Fortaleza, Ceara, Brazil
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Hong Cheng , David Lo , Yang Zhou , Xiaoyin Wang , Xifeng Yan, Identifying bug signatures using discriminative graph mining, Proceedings of the eighteenth international symposium on Software testing and analysis, July 19-23, 2009, Chicago, IL, USA
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