Ask the Mutants: Mutating Faulty Programs for Fault Localization

Abstract

Abstract-We present MUSE (MUtation-baSEd fault localization technique), a new fault localization technique based on mutation analysis. A key idea of MUSE is to identify a faulty statement by utilizing different characteristics of two groups of mutants-one that mutates a faulty statement and the other that mutates a correct statement. We also propose a new evaluation metric for fault localization techniques based on information theory, called Locality Information Loss (LIL): it can measure the aptitude of a localization technique for automated fault repair systems as well as human debuggers. The empirical evaluation using 14 faulty versions of the five real-world programs shows that MUSE localizes a fault after reviewing 7.4 statements on average, which is about 25 times more precise than the state-of-the-art SBFL technique Op2.

An Investigation into the Use of Mutation Analysis for Automated Program Repair

“…We now address Mutation-Based Fault Localisation (MBFL), a relatively new approach based on mutation analysis [14,16]. This analysis generates and evaluates a large number of mutants on the test cases.…”

“…Each mutant is a variant of the original program, obtained by applying a traditional mutation testing operator (e.g., flip comparison operator) at a single location [16]. Two seminal approaches to MBFL are MUSE [14] and Metallaxis [16]. Both of these approaches share a common intuition: mutants generated at the fault location should exhibit different test suite outcomes to those generated at non-faulty locations.…”

“…Inspired by recent work in mutation testing [16,14], we propose to use candidate test suite evaluations to identify suitable fix locations online. Mutationbased fault localisation show promising results when ranking statements as candidates for human modification; We explicitly evaluate their utility in assigning suspiciousness scores to candidate repair locations, the key concern in localisation for repair.…”

“…To determine whether the results of candidate patch evaluations may be used to localise the fault, we first perform a mutation analysis on a sample of a particular G&V repair search space across 28 bugs in six real-world C programs. We use the same ground truth as previous studies on fault localisation, assuming the location(s) of the human-written repair or the injected fault to be a suitable fix location [14,16,25]. For the sake of convenience, we refer to these locations as "faulty"; non-modified statements are considered to be "correct".…”

A Survey of Test Based Automatic Program Repair

“…However, mergence of fault localization and repairing is a considerable alternative. The idea of Mutation based localization [68] can provide some hints for that. It takes a statement as suspicious when a mutation on it switches some failed runs to pass, meanwhile the mutation can be a candidate patch in test based repair.…”