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 realworld systems (both open and closed source). The results show that our proposal can significantly (p ≤ 0.05) improve fault prediction performance. Moreover, mutationbased 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.
Recent work by Bowes et al. REF propose a mutation-aware fault prediction technique, which leverages guidance from mutation analysis to construct dynamic metrics.
Mutation-aware fault prediction
ISSTA 2016
Computer Science