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• W2016029449 abstract "When failures occur during software testing, automated software fault localization helps to diagnose their root causes and identify the defective statements of a program to support debugging. Diagnosis is carried out by selecting test cases in such way that their pass or fail information will narrow down the set of fault candidates, and, eventually, pinpoint the root cause. An essential in gredient of effective and efficient fault localization is knowledge about the false negative rate of tests, which is related to the rate at which defective statements of a program will exhibit failures. In current fault localization processes, false negative rates are either ignored completely, or merely estimated a posteriori as part of the diagnosis. In this paper, we study the reduction in diagnosis effort when false negative rates are known a priori. We deduce this information from testability, following the propagation-infection-execution (PIE) approach. Experiments with real programs suggest significant improvement in the diagnosis process, both in the single and the multiple-fault cases. When compared to the next-best technique, PIE-based false negative rate information yields a fault localization effort reduction of up to 80% for systems with only one fault, and up to 60% for systems with multiple faults." @default.
• W2016029449 created "2016-06-24" @default.
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• W2016029449 date "2011-03-21" @default.
• W2016029449 modified "2023-10-18" @default.
• W2016029449 title "An empirical study on the usage of testability information to fault localization in software" @default.
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• W2016029449 doi "https://doi.org/10.1145/1982185.1982489" @default.
• W2016029449 hasPublicationYear "2011" @default.
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