Every Mutation Should Be Rewarded: Boosting Fault Localization with Mutated Predicates
Xuezheng Xu, Changwei Zou, Jingling Xue · 2020
Many fault localization (FL) techniques have been proposed to facilitate software debugging. Due to being lightweight, spectrum-based fault localization (SBFL) is one of the most popular FL families and widely deployed in program repair tools. SBFL ranks program elements by recording the program coverage under a test suite and calculates the suspiciousness score of each element with a ranking formula. Despite numerous formulae proposed, SBFL still suffers from providing no new sources of information other than program coverage. Mutation-based fault localization (MBFL) iteratively mutates a faulty program and suggests fault locations through mutants that overturn failed test cases. However, due to its explosive search space, MBFL has been adopted by only few program repair tools.In this paper, we aim at exploiting the advantages of MBFL and boosting SBFL with low overhead by building a practical FL tool. We propose Flip, an FL technique with inferences from mutated predicates. Based on SBFL, we leverage and extend the predicate switching technique to infer fault locations no matter whether the mutated predicate can overturn a failed test case or not. Finally, we compute a new ranking list with a joint inference that combines program coverage and mutation inferences.We use Defects4j (version 1.5.0), containing 438 real-world faults from six projects to evaluate Flip. All the seven stateof-the-art SBFL techniques benefit from Flip (e.g., by ranking up to 46.4% more faults in top-1) with low overhead (e.g., by incurring less than 2-minute average overhead for each fault). We also offer some insights on how to further improve FL on real-world faults based on the empirical results.