Deriving products for variability test of Feature Models with a hyper-heuristic approach |
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Affiliation: | 1. Department of Statistics, Feng Chia University, Taichung 40724, Taiwan;2. School of Maritime Economics & Management, Dalian Maritime University, Dalian 116026, China;3. Department of Industrial Engineering & Management, Cheng Shiu University, Kaohsiung 83347, Taiwan;4. School of Logistics, Central South University of Forestry and Technology, Changsha 410004, China;5. Graduate Institute of Business Administration, Cheng Shiu University, Kaohsiung City 83347, Taiwan |
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Abstract: | Deriving products from a Feature Model (FM) for testing Software Product Lines (SPLs) is a hard task. It is important to select a minimum number of products but, at the same time, to consider the coverage of testing criteria such as pairwise, among other factors. To solve such problems Multi-Objective Evolutionary Algorithms (MOEAs) have been successfully applied. However, to design a solution for this and other software engineering problems can be very difficult, because it is necessary to choose among different search operators and parameters. Hyper-heuristics can help in this task, and have raised interest in the Search-Based Software Engineering (SBSE) field. Considering the growing adoption of SPL in the industry and crescent demand for SPL testing approaches, this paper introduces a hyper-heuristic approach to automatically derive products to variability testing of SPLs. The approach works with MOEAs and two selection methods, random and based on FRR-MAB (Fitness Rate Rank based Multi-Armed Bandit). It was evaluated with real FMs and the results show that the proposed approach outperforms the traditional algorithms used in the literature, and that both selection methods present similar performance. |
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Keywords: | Software Product Line Software testing Hyper-heuristic |
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