Mutation analysis can effectively capture the dependency between source code and test results. This has been exploited by Mutation Based Fault Localisation (MBFL) techniques. However, MBFL techniques suffer from the need to expend the high cost of mutation analysis after the observation of failures, which may present a challenge for its practical adoption. We introduce SIMFL (Statistical Inference for Mutation-based Fault Localisation), an MBFL technique that allows users to perform the mutation analysis in advance against an earlier version of the system. SIMFL uses mutants as artificial faults and aims to learn the failure patterns among test cases against different locations of mutations. Once a failure is observed, SIMFL requires either almost no or very small additional cost for analysis, depending on the used inference model. An empirical evaluation of SIMFL using 355 faults in Defects4J shows that SIMFL can successfully localise up to 103 faults at the top, and 152 faults within the top five, on par with state-of-the-art alternatives. The cost of mutation analysis can be further reduced by mutation sampling: SIMFL retains over 80 of its localisation accuracy at the top rank when using only 10 of generated mutants, compared to results obtained without sampling.
Mutation analysis has been used to improve statistical software fault prediction models, by @cite_35 . By adding 40 mutation-based metrics calculated based on information collected by running the publicly available PITest mutation testing tool to 39 traditional, static source code metrics the study showed improvements in predictive performance. The models predicted which classes where faulty rather than helping rank and locate specific faults at the method level as we do here.
@cite_35
2471516951 1950030762 1990785546 1980199047
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. Fault localization methods seek to identify faulty program statements based on the information provided by the failing and passing test executions. Spectrum-based methods are among the most popular ones and assist programmers by assigning suspiciousness values on program statements according to their probability of being faulty. This paper proposes Met allaxis, a fault localization approach based on mutation analysis. The innovative part of Met allaxis is that it uses mutants and links them with the faulty program places. Thus, mutants that are killed mostly by failing tests provide a good indication about the location of a fault. Experimentation using Met allaxis suggests that it is significantly more effective than statement-based approaches. This is true even in the case where mutation cost-reduction techniques, such as mutant sampling, are facilitated. Additionally, results from a controlled experiment show that the use of mutation as a testing technique provides benefits to the fault localization process. Therefore, fault localization is significantly improved by using mutation-based tests instead of block-based or branch-based test suites. Finally, evidence in support of the methods' scalability is also given. Copyright © 2013 John Wiley & Sons, Ltd. 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. Neutral landscapes and mutational robustness are believed to be important enablers of evolvability in biology. We apply these concepts to software, defining mutational robustness to be the fraction of random mutations to program code that leave a program's behavior unchanged. Test cases are used to measure program behavior and mutation operators are taken from earlier work on genetic programming. Although software is often viewed as brittle, with small changes leading to catastrophic changes in behavior, our results show surprising robustness in the face of random software mutations. The paper describes empirical studies of the mutational robustness of 22 programs, including 14 production software projects, the Siemens benchmarks, and four specially constructed programs. We find that over 30 of random mutations are neutral with respect to their test suite. The results hold across all classes of programs, for mutations at both the source code and assembly instruction levels, across various programming languages, and bear only a limited relation to test suite coverage. We conclude that mutational robustness is an inherent property of software, and that neutral variants (i.e., those that pass the test suite) often fulfill the program's original purpose or specification. Based on these results, we conjecture that neutral mutations can be leveraged as a mechanism for generating software diversity. We demonstrate this idea by generating a population of neutral program variants and showing that the variants automatically repair latent bugs. Neutral landscapes also provide a partial explanation for recent results that use evolutionary computation to automatically repair software bugs.