Seeding Strategies for Multi-Objective Test Case Selection An Application on Simulation-based Testing

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.