Combinatorial Test Suite Generation Strategy Using Enhanced Sine Cosine Algorithm

Abstract

Owing to its simplicity and having no control parameters, the Sine Cosine Algorithm (SCA) has attracted much attention among researchers. Although useful, the SCA algorithm adopts a linear magnitude update to determine its sine or cosine position updates. In the actual searching process, the magnitude update is rarely linear. In fact, the magnitude update is also non-exponential and is highly dependent on the problem domain and its search topology. For this reason, our work proposes a combination of linear and exponential magnitude update for the search displacement. In doing so, we adopt the combinatorial testing problem as our case study. Combinatorial testing strategies generate test data which cover all required interactions among parameter values of a system-under-test in order to explore interaction faults. Our evaluation gives promising results on the improved performance over the original SCA algorithm. As far as test data generation time is concerned, the enhanced SCA outperformed all its counterparts, whereas its results in terms of test suite sizes are comparable to other parameter free meta-heuristic algorithms.