Epistasis Based ACO for Regression Test Case Prioritization
Yi Bian, Zheng Li, Ruilian Zhao, Dunwei Gong · IEEE Transactions on Emerging Topics in Computational Intelligence · 2017
Metaheuristics that are inspired by natural systems have been widely applied into search-based software engineering. It has been shown that combining knowledge of the application domain with a biological theory for metaheuristics can narrow down the search space and speed up the convergence for metaheuristics based algorithms. This paper introduces Epistatic Test case Segment (ETS) for multiobjective search-based regression Test Case Prioritization (MoTCP), based on epistasis theory that reflects the correlation between genes in evolution process. An ETS-based pheromone update strategy for ant colony optimization (ACO) algorithm is proposed. The experiments with three benchmarks and a real industrial program V8 illustrate that proposed pheromone update strategy guided by the epistasis theory can significant improve the performance of ACO in terms of effectiveness and efficiency for search-based MoTCP.