Abstract
Automatic test case generation has been received great attention by researchers. Evolutionary algorithms have increasingly gained special places as means of automating the test data generation for software testing. Genetic algorithm (GA) is the most commonplace algorithm in search-based software testing. One of the key issues of search-based testing is the inefficient and inadequate informed fitness function due to the rigidness of fitness landscape. To deal with this problem, in this paper we improved a recently published fundamental approach where a new criterion, branch hardness factor is used to calculate fitness. However, the existing methods are unable to cover the whole of the targets. Herein, we added a local search strategy to the standard GA for faster convergence and providing more intensification. In addition, different selection and mutation operators are examined and appropriate choices selected. Our approach gained remarkable efficiencies on 7 standard benchmarks. The results showed that adding local search is likely to boost another search-based algorithm for path coverage even.
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Hasheminasab, Z., Sharifi, Z., Soltanian, K., Afsharchi, M. (2020). Using Augmented Genetic Algorithm for Search-Based Software Testing. In: Bohlouli, M., Sadeghi Bigham, B., Narimani, Z., Vasighi, M., Ansari, E. (eds) Data Science: From Research to Application. CiDaS 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 45. Springer, Cham. https://doi.org/10.1007/978-3-030-37309-2_20
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