Goiuria Sagardui

The time it takes software systems to be tested is usually long. Search-based test selection has been a widely investigated technique to optimize the testing process. In this article, we propose a set of seeding strategies for the test case selection problem that generates the initial population of Pareto-based multi-objective algorithms, with the goals of (1) helping to find an overall better set of solutions and (2) enhancing the convergence of the algorithms. The seeding strategies were integrated with four state-of-the-art multi-objective search algorithms and applied into two contexts where regression-testing is paramount: (1) Simulation-based testing of Cyber-physical Systems and (2) Continuous Integration. For the first context, we evaluated our approach by using six fitness function combinations and six independent case studies, whereas in the second context, we derived a total of six fitness function combinations and employed four case studies. Our evaluation suggests that some of the proposed seeding strategies are indeed helpful for solving the multi-objective test case selection problem. Specifically, the proposed seeding strategies provided a higher convergence of the algorithms towards optimal solutions in 96% of the studied scenarios and an overall cost-effectiveness with a standard search budget in 85% of the studied scenarios.

The digital industry requires increasingly complex and reliable software systems. They need to control and make critical decisions at runtime. As a consequence, the verification and validation of these systems has become a major research challenge. At design and development time, model testing techniques are used while run-time verification aims at verifying that a system satisfies a given property. The latter technique complements the former. The solution presented in this paper targets embedded systems whose software components are designed by state machines defined by Unified Modelling Language (UML). The CRESCO (C++ REflective State-Machines based observable software COmponents) platform generates software components that provide internal information at runtime and the verifier uses this information to check system-level reliability/safety contracts. The verifier detects when a system contract is violated and initiates a safeState process to prevent dangerous scenarios. These contracts are defined by internal information from the software components that make up the system. Thus, as demonstrated in the tested experiment, the robustness of the system is increased. All software components (controllers), such as the verifier, have been deployed as services (producers/consumers) of the Arrowhead IoT platform: the controllers are deployed on local Arrowhead platforms (Edge) and the verifier (Safety Manager) is deployed on an Arrowhead platform (Cloud) that will consume controllers on the Edge and ensure the proper functioning of the plant controllers. Keywords: run-time monitoring, robustness, software components, contracts, software models, state machines