Izaskun Fernandez, Cristina Aceta, Cristina Fernandez, Maria Ines Torres, Aitor Etxalar, Ariane Mendez, Maia Agirre, Manuel Torralbo, Arantza Del Pozo, Joseba Agirre, Egoitz Artetxe, Iker Altuna. Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue. 2024.

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 time it takes software systems to be tested is usually long. This is often caused by the time it takes the entire test suite to be executed. To optimize this, regression test selection approaches have allowed for improvements to the cost-effectiveness of verification and validation activities in the software industry. In this area, multi-objective algorithms have played a key role in selecting the appropriate subset of test cases from the entire test suite. In this paper, we propose a set of seeding strategies for the test case selection problem that generate the initial population of multi-objective algorithms. We integrated these seeding strategies with an NSGA-II algorithm for solving the test case selection problem in the context of simulation-based testing. We evaluated the strategies with six case studies and a total of 21 fitness combinations for each case study (i.e., a total of 126 problems). Our evaluation suggests that these strategies are indeed helpful for solving the multi-objective test case selection problem. In fact, two of the proposed seeding strategies outperformed the NSGA-II algorithm without seeding population with statistical significance for 92.8 and 96% of the problems.

The use of Model Driven Development (MDD) is increasing in industry. When a Non Functional Requirement (NFR) not considered in the development must be added metamodels, models and also transformations are affected. Tasks for defining and maintaining model transformation rules can be complex in MDD. Model Transformation By Example (MTBE) approaches have been proposed to ease the development of transformation rules. In this paper an approach based on MTBE to derive the adaptation operations that must be implemented in a legacy model transformation when a NFR appears is presented. The approach derives semi-automatically the model transformations using execution traceability data and models differences. An example where access control property is integrated on a MDD system is introduced to demonstrate the usefulness of the tool to evolve model transformations.