Complete Agent-driven Model-based System Testing for Autonomous Systems

In this position paper, a novel approach to testing complex autonomous transportation systems (ATS) in the automotive, avionic, and railway domains is described. It is intended to mitigate some of the most critical problems regarding verification and validation (V&V) effort for ATS. V&V is known to become infeasible for complex ATS, when using conventional methods only. The approach advocated here uses complete testing methods on the module level, because these establish formal proofs for the logical correctness of the software. Having established logical correctness, system-level tests are performed in simulated cloud environments and on the target system. To give evidence that 'sufficiently many' system tests have been performed with the target system, a formally justified coverage criterion is introduced. To optimise the execution of very large system test suites, we advocate an online testing approach where multiple tests are executed in parallel, and test steps are identified on-the-fly. The coordination and optimisation of these executions is achieved by an agent-based approach. Each aspect of the testing approach advocated here is shown to either be consistent with existing standards for development and V&V of safety-critical transportation systems, or it is justified why it should become acceptable in future revisions of the applicable standards.Comment: In Proceedings FMAS 2021, arXiv:2110.1152

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