Abstract
The very large number of test cases and time consumption for a test, it is becoming hard to perform exhaustive testing for any software fault detection. For this reason, combinatorial testing (CT) also known as t-way testing, is one of the well-known methods that are used for fault detections to many software systems. Various existing research works are available in the literature to minimize the number of test cases, and the time to obtain an optimal test suite or competitive test suite. However, the interaction strength of the existing research works are supports up to t = 2 or t = 3, where t is the strength of parameter’s interaction. The major purpose of this research is to suggest a new t-way strategy to minimize the test cases. This is called hybrid artificial bee colony (HABC) strategy, which is based on hybridize of an artificial bee colony (ABC) algorithm with a particle swarm optimization (PSO) algorithm. This is to provide a high-interaction strength combinatorial test suite up to t = 6. From experimental results, HABC strategy performed best when compared with existing methods in terms of generating the optimum test case.
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Alazzawi, A.K., Rais, H.M., Basri, S. (2019). Hybrid Artificial Bee Colony Algorithm for t-Way Interaction Test Suite Generation. In: Silhavy, R. (eds) Software Engineering Methods in Intelligent Algorithms. CSOC 2019. Advances in Intelligent Systems and Computing, vol 984. Springer, Cham. https://doi.org/10.1007/978-3-030-19807-7_19
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