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Empirical evaluation of the tarantula automatic fault-localization technique
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Source Automated Software Engineering archive
Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering table of contents
Long Beach, CA, USA
SESSION: Testing II table of contents
Pages: 273 - 282  
Year of Publication: 2005
ISBN:1-59593-993-4
Authors
James A. Jones  Georgia Institute of Technology, Atlanta, GA
Mary Jean Harrold  Georgia Institute of Technology, Atlanta, GA
Sponsors
ACM: Association for Computing Machinery
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGSOFT: ACM Special Interest Group on Software Engineering
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 21,   Downloads (12 Months): 144,   Citation Count: 29
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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.


REFERENCES

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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H. Agrawal, J. Horgan, S. London, and W. Wong. Fault localization using execution slices and dataflow tests. In Proceedings of IEEE Software Reliability Engineering, pages 143--151, 1995.
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J. Jones, M. J. Harrold, and J. Stasko. Visualization for fault localization. In Proceedings of the Workshop on Software Visualization, 23rd International Conference on Software Engineering, Toronto, Ontario, May 2001.
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H. Pan and E. Spafford. Heuristics for automatic localization of software faults. Technical Report SERC-TR-116-P, Purdue University, 1992.
 
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M. Renieris and S. Reiss. Fault localization with nearest neighbor queries. In Proceedings of the International Conference on Automated Software Engineering, pages 30--39, Montreal, Quebec, October 2003.
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14
I. Vessey. Expertise in debugging computer programs. International Journal of Man-Machine Studies: A process analysis, 23(5):459--494, 1985.
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CITED BY  29

Collaborative Colleagues:
James A. Jones: colleagues
Mary Jean Harrold: colleagues