Mutation-aware fault prediction

David Bowes, Tracy Hall, Mark Harman, Yue Jia, Federica Sarro, and Fan Wu, 'Mutation-aware fault prediction', in Proceedings of the 25th International Symposium on Software Testing and Analysis, ISSTA 2016. Saarbrucken, Germany, 18-20 July September 2016. Andreas Zeller and Abhik Roychoudhury eds., e-ISBN 978-145034390-9, doi: 10.1145/2931037.2931039. The ACM Digital Library is published by the Association for Computing Machinery. Copyright © 2017 ACM, Inc.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 Different 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