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,