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Title: Mutation-aware fault prediction
Authors: Bowes, D
Hall, T
Harman, M
Jia, Y
Sarro, F
Wu, F
Keywords: Software Fault Prediction;Software Defect Prediction;Mutation Testing;Software Metrics;Empirical Study
Issue Date: 2016
Citation: ISSTA 2016 - Proceedings of the 25th International Symposium on Software Testing and Analysis, 2016, pp. 330 - 341
Abstract: We introduce mutation-aware fault prediction, which leverages additional guidance from metrics constructed in terms of mutants and the test cases that cover and detect them. We report the results of 12 sets of experiments, applying 4 di↵erent predictive modelling techniques to 3 large real world systems (both open and closed source). The results show that our proposal can significantly (p 0.05) improve fault prediction performance. Moreover, mutation based metrics lie in the top 5% most frequently relied upon fault predictors in 10 of the 12 sets of experiments, and provide the majority of the top ten fault predictors in 9 of the 12 sets of experiments.
Appears in Collections:Dept of Computer Science Research Papers

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