Incorporating User Preferences in a Software Product Line Testing Hyper-Heuristic Approach

摘要

To perform the variability testing of Software Product Lines (SPLs) a set of products, represented in the Feature Model (FM), should be selected. Such selection is impacted by conflicting factors and has been efficiently solved by Evolutionary Multi-objective Algorithms in combination with hyper-heuristics. However, many times there is a cost budget or coverage level to be satisfied during the test, which are difficult to be incorporated as objective functions. Due to this, the choice of the best solution to be used in practice is not always easy. To deal with this situation, this paper introduces a preference-based hyper-heuristic approach to solve this problem. The approach implements the preference-based algorithm r-NSGA-II working with the random and FRRMAB selection methods. This last one uses a reward function based on r-dominance concept that takes into consideration a Reference Point provided by the tester. Our approach outperforms existing approaches, as well as the traditional algorithm r-NSGA-II, generating a reduced number of non-interesting solutions from the tester's point of view, that is, considering the provided Region of Interest (ROI).