Selecting fault revealing mutants

Mutant selection refers to the problem of choosing, among a large number of mutants, the (few) ones that should be used by the testers. In view of this, we investigate the problem of selecting the fault revealing mutants, i.e., the mutants that are killable and lead to test cases that uncover unknown program faults. We formulate two variants of this problem: the fault revealing mutant selection and the fault revealing mutant prioritization. We argue and show that these problems can be tackled through a set of ‘static’ program features and propose a machine learning approach, named FaRM, that learns to select and rank killable and fault revealing mutants. Experimental results involving 1,692 real faults show the practical benefits of our approach in both examined problems. Our results show that FaRM achieves a good trade-off between application cost and effectiveness (measured in terms of faults revealed). We also show that FaRM outperforms all the existing mutant selection methods, i.e., the random mutant sampling, the selective mutation and defect prediction (mutating the code areas pointed by defect prediction). In particular, our results show that with respect to mutant selection, our approach reveals 23% to 34% more faults than any of the baseline methods, while, with respect to mutant prioritization, it achieves higher average percentage of revealed faults with a median difference between 4% and 9% (from the random mutant orderings)

Selecting fault revealing mutants

Mutant selection refers to the problem of choosing, among a large number of mutants, the (few) ones that should be used by the testers. In view of this, we investigate the problem of selecting the fault revealing mutants, i.e., the mutants that are killable and lead to test cases that uncover unknown program faults. We formulate two variants of this problem: the fault revealing mutant selection and the fault revealing mutant prioritization. We argue and show that these problems can be tackled through a set of ‘static’ program features and propose a machine learning approach, named FaRM, that learns to select and rank killable and fault revealing mutants. Experimental results involving 1,692 real faults show the practical benefits of our approach in both examined problems. Our results show that FaRM achieves a good trade-off between application cost and effectiveness (measured in terms of faults revealed). We also show that FaRM outperforms all the existing mutant selection methods, i.e., the random mutant sampling, the selective mutation and defect prediction (mutating the code areas pointed by defect prediction). In particular, our results show that with respect to mutant selection, our approach reveals 23% to 34% more faults than any of the baseline methods, while, with respect to mutant prioritization, it achieves higher average percentage of revealed faults with a median difference between 4% and 9% (from the random mutant orderings)

Assessment and Improvement of the Practical Use of Mutation for Automated Software Testing

Software testing is the main quality assurance technique used in software engineering. In fact, companies that develop software and open-source communities alike actively integrate testing into their software development life cycle. In order to guide and give objectives for the software testing process, researchers have designed test adequacy criteria (TAC) which, define the properties of a software that must be covered in order to constitute a thorough test suite. Many TACs have been designed in the literature, among which, the widely used statement and branch TAC, as well as the fault-based TAC named mutation. It has been shown in the literature that mutation is effective at revealing fault in software, nevertheless, mutation adoption in practice is still lagging due to its cost. Ideally, TACs that are most likely to lead to higher fault revelation are desired for testing and, the fault-revelation of test suites is expected to increase as their coverage of TACs test objectives increase. However, the question of which TAC best guides software testing towards fault revelation remains controversial and open, and, the relationship between TACs test objectives’ coverage and fault-revelation remains unknown. In order to increase knowledge and provide answers about these issues, we conducted, in this dissertation, an empirical study that evaluates the relationship between test objectives’ coverage and fault-revelation for four TACs (statement, branch coverage and, weak and strong mutation). The study showed that fault-revelation increase with coverage only beyond some coverage threshold and, strong mutation TAC has highest fault revelation. Despite the benefit of higher fault-revelation that strong mutation TAC provide for software testing, software practitioners are still reluctant to integrate strong mutation into their software testing activities. This happens mainly because of the high cost of mutation analysis, which is related to the large number of mutants and the limitation in the automation of test generation for strong mutation. Several approaches have been proposed, in the literature, to tackle the analysis’ cost issue of strong mutation. Mutant selection (reduction) approaches aim to reduce the number of mutants used for testing by selecting a small subset of mutation operator to apply during mutants generation, thus, reducing the number of analyzed mutants. Nevertheless, those approaches are not more effective, w.r.t. fault-revelation, than random mutant sampling (which leads to a high loss in fault revelation). Moreover, there is not much work in the literature that regards cost-effective automated test generation for strong mutation. This dissertation proposes two techniques, FaRM and SEMu, to reduce the cost of mutation testing. FaRM statically selects and prioritizes mutants that lead to faults (fault-revealing mutants), in order to reduce the number of mutants (fault-revealing mutants represent a very small proportion of the generated mutants). SEMu automatically generates tests that strongly kill mutants and thus, increase the mutation score and improve the test suites. First, this dissertation makes an empirical study that evaluates the fault-revelation (ability to lead to tests that have high fault-revelation) of four TACs, namely statement, branch, weak mutation and strong mutation. The outcome of the study show evidence that for all four studied TACs, the fault-revelation increases with TAC test objectives’ coverage only beyond a certain threshold of coverage. This suggests the need to attain higher coverage during testing. Moreover, the study shows that strong mutation is the only studied TAC that leads to tests that have, significantly, the highest fault-revelation. Second, in line with mutant reduction, we study the different mutant quality indicators (used to qualify "useful" mutants) proposed in the literature, including fault-revealing mutants. Our study shows that there is a large disagreement between the indicators suggesting that the fault-revealing mutant set is unique and differs from other mutant sets. Thus, given that testing aims to reveal faults, one should directly target fault-revealing mutants for mutant reduction. We also do so in this dissertation. Third, this dissertation proposes FaRM, a mutant reduction technique based on supervised machine learning. In order to automatically discriminate, before test execution, between useful (valuable) and useless mutants, FaRM build a mutants classification machine learning model. The features for the classification model are static program features of mutants categorized as mutant types and mutant context (abstract syntax tree, control flow graph and data/control dependency information). FaRM’s classification model successfully predicted fault-revealing mutants and killable mutants. Then, in order to reduce the number of analyzed mutants, FaRM selects and prioritizes fault-revealing mutants based of the aforementioned mutants classification model. An empirical evaluation shows that FaRM outperforms (w.r.t. the accuracy of fault-revealing mutant selection) random mutants sampling and existing mutation operators-based mutant selection techniques. Fourth, this dissertation proposes SEMu, an automated test input generation technique aiming to increase strong mutation coverage score of test suites. SEMu is based on symbolic execution and leverages multiple cost reduction heuristics for the symbolic execution. An empirical evaluation shows that, for limited time budget, the SEMu generates tests that successfully increase strong mutation coverage score and, kill more mutants than test generated by state-of-the-art techniques. Finally, this dissertation proposes Muteria a framework that enables the integration of FaRM and SEMu into the automated software testing process. Overall, this dissertation provides insights on how to effectively use TACs to test software, shows that strong mutation is the most effective TAC for software testing. It also provides techniques that effectively facilitate the practical use of strong mutation and, an extensive tooling to support the proposed techniques while enabling their extensions for the practical adoption of strong mutation in software testing

Empirical Evaluation of Mutation-based Test Prioritization Techniques

We propose a new test case prioritization technique that combines both mutation-based and diversity-based approaches. Our diversity-aware mutation-based technique relies on the notion of mutant distinguishment, which aims to distinguish one mutant's behavior from another, rather than from the original program. We empirically investigate the relative cost and effectiveness of the mutation-based prioritization techniques (i.e., using both the traditional mutant kill and the proposed mutant distinguishment) with 352 real faults and 553,477 developer-written test cases. The empirical evaluation considers both the traditional and the diversity-aware mutation criteria in various settings: single-objective greedy, hybrid, and multi-objective optimization. The results show that there is no single dominant technique across all the studied faults. To this end, \rev{we we show when and the reason why each one of the mutation-based prioritization criteria performs poorly, using a graphical model called Mutant Distinguishment Graph (MDG) that demonstrates the distribution of the fault detecting test cases with respect to mutant kills and distinguishment

MuDelta: Delta-Oriented Mutation Testing at Commit Time

To effectively test program changes using mutation testing, one needs to use mutants that are relevant to the altered program behaviours. In view of this, we introduce MuDelta, an approach that identifies commit-relevant mutants; mutants that affect and are affected by the changed program behaviours. Our approach uses machine learning applied on a combined scheme of graph and vector-based representations of static code features. Our results, from 50 commits in 21 Coreutils programs, demonstrate a strong prediction ability of our approach; yielding 0.80 (ROC) and 0.50 (PR Curve) AUC values with 0.63 and 0.32 precision and recall values. These predictions are significantly higher than random guesses, 0.20 (PR-Curve) AUC, 0.21 and 0.21 precision and recall, and subsequently lead to strong relevant tests that kill 45%more relevant mutants than randomly sampled mutants (either sampled from those residing on the changed component(s) or from the changed lines). Our results also show that MuDelta selects mutants with 27% higher fault revealing ability in fault introducing commits. Taken together, our results corroborate the conclusion that commit-based mutation testing is suitable and promising for evolving software

Squeeziness: An information theoretic measure for avoiding fault masking

Copyright @ 2012 ElsevierFault masking can reduce the effectiveness of a test suite. We propose an information theoretic measure, Squeeziness, as the theoretical basis for avoiding fault masking. We begin by explaining fault masking and the relationship between collisions and fault masking. We then define Squeeziness and demonstrate by experiment that there is a strong correlation between Squeeziness and the likelihood of collisions. We conclude with comments on how Squeeziness could be the foundation for generating test suites that minimise the likelihood of fault masking

An Empirical Study of Off-by-one Loop Mutation

Context: Developing test cases that are measurably effective in finding faults in programs is a very challenging research problem. Mutation testing, a prominent technique developed to address this challenge, often becomes com- putationally too expensive for practical use due to the very large number of mutants that need to be analyzed. Objective: This paper evaluates the impact of One-by-one (OBO) loop mutation in reducing the cost of mutation analysis and investigates this technique\u27s effectiveness in measuring the strength or weakness of test suites. Method: A set of Java and C programs have been used to generate both OBO and traditional mutants. Mutation scores are computed and analyzed for both sets of mutants. An analysis of first order vs. higher order loop mutations have also been performed. Results: On average, 89.15% fewer mutants are generated by OBO op- erator in comparison to traditional operators while the two sets of muta- tion scores still remain highly positively correlated (correlation coefficient of .9228) indicating the usefulness of OBO operator in measuring test suite\u27s ef- fectiveness of finding faults in programs. We also investigate the relationship between first order OBO mutation (FOM) and their corresponding higher order mutations (HOM). We have found that OBO HOMs do not subsume their corresponding FOMs. Conclusion: We conclude that One-by-one (OBO) loop mutant operator, which targets specific program elements for mutation, can greatly reduce the number of mutants generated, and thus make the mutation analysis relatively inexpensive and practical while still being capable of providing useful measurement of the strength or weakness of a test suite. Our investigation into the relationship between higher order OBO mutants (HOM) and first order OBO mutants (FOM) has revealed that OBO HOMs usually do not add any value to the mutation analysis over the corresponding FOMs