Applying Meta-Heuristic Algorithm based on Slicing Input Variables to Support Automated Test Data Generation

KIPS Transactions on Software and Data Engineering, Vol. 7, No.1, pp.1-8, January 2018
10.3745/KTSDE.2018.7.1.001, Full Text

Abstract

Software testing is important to determine the reliability of the system, a task that requires a lot of effort and cost. Model-based testing has been proposed as a way to reduce these costs by automating test designs from models that regularly represent system requirements. For each path of model to generate an input value to perform a test, meta-heuristic technique is used to find the test data. In this paper, we propose an automatic test data generation method using a slicing method and a priority policy, and suppress unnecessary computation by excluding variables not related to target path. And then, experimental results show that the proposed method generates test data more effectively than conventional method.


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Cite this paper

[KIPS Transactions Style]
H. Choi and B. Lee, "Applying Meta-Heuristic Algorithm based on Slicing Input Variables to Support Automated Test Data Generation," KIPS Transactions on Software and Data Engineering, Vol.7, No.1, pp.1-8, 2018, DOI: 10.3745/KTSDE.2018.7.1.001.

[IEEE Style]
Hyorin Choi and Byungjeong Lee, "Applying Meta-Heuristic Algorithm based on Slicing Input Variables to Support Automated Test Data Generation," KIPS Transactions on Software and Data Engineering, vol. 7, no. 1, pp. 1-8, 2018. DOI: 10.3745/KTSDE.2018.7.1.001.

[ACM Style]
Choi, H. and Lee, B. 2018. Applying Meta-Heuristic Algorithm based on Slicing Input Variables to Support Automated Test Data Generation. KIPS Transactions on Software and Data Engineering, 7, 1, (2018), 1-8. DOI: 10.3745/KTSDE.2018.7.1.001.