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Motivation

Theory and Practice, Do They Match? A Case With Spectrum-Based Fault Localization. Tien-Duy B. Le, Ferdian Thung , and David Lo {btdle.2012,ferdiant.2013,davidlo}@smu.edu.sg. Motivation. Result. Theoretically Best SBFL Formula by Xie et. a l. Popular SBFL Formu la.

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Motivation

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  1. Theory and Practice, Do They Match? A Case With Spectrum-Based Fault Localization Tien-Duy B. Le, FerdianThung, and David Lo {btdle.2012,ferdiant.2013,davidlo}@smu.edu.sg Motivation Result Theoretically Best SBFL Formula by Xie et. al. Popular SBFL Formula Effectiveness Measure Real Life Programs vs. Effectiveness of The SBFL Formulas • Average test coverage in our dataset is 84.97%. ? Spectrum Based Fault Localization (SBFL) • Wilcoxon signed rank test (significancerate of 0.05) • Ochiai is statistically better than ER5a, ER5b, ER5c Definition a technique to localize failures to certain parts of a program by utilizing program spectra collected from software testing and the results of the tests (pass or fail) Statistics for SBFL Popular SBFL Formula Dataset Conclusion Reference • Theoretically best SBFL formulas are proven the best in theory, but in practice: • They do not work as well: • Ochiai outperforms all of them. • Tarantula outperforms four of them. • The 100% test coverage assumption does not hold. Theoretically Best SBFL Formula • X. Xie, T. Chen, F.-C. Kuo, and B. Xu, “A theoretical analysis of the risk evaluation formulas for spectrum-based fault localization,” TOSEM (to appear), 2013. • Lucia, D. Lo, L. Jiang, and A. Budi, “Comprehensive evaluation of association measures for fault localization,” in ICSM, 2010. • Notable assumption: 100% test coverage

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