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Metaheuristic Techniques for Test Case Generation: A Review

Metaheuristic Techniques for Test Case Generation: A Review

Rashmi Rekha Sahoo, Mitrabinda Ray
Copyright: © 2018 |Volume: 11 |Issue: 1 |Pages: 14
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781522543206|DOI: 10.4018/JITR.2018010110
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MLA

Sahoo, Rashmi Rekha, and Mitrabinda Ray. "Metaheuristic Techniques for Test Case Generation: A Review." JITR vol.11, no.1 2018: pp.158-171. http://doi.org/10.4018/JITR.2018010110

APA

Sahoo, R. R. & Ray, M. (2018). Metaheuristic Techniques for Test Case Generation: A Review. Journal of Information Technology Research (JITR), 11(1), 158-171. http://doi.org/10.4018/JITR.2018010110

Chicago

Sahoo, Rashmi Rekha, and Mitrabinda Ray. "Metaheuristic Techniques for Test Case Generation: A Review," Journal of Information Technology Research (JITR) 11, no.1: 158-171. http://doi.org/10.4018/JITR.2018010110

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Abstract

The primary objective of software testing is to locate bugs as many as possible in software by using an optimum set of test cases. Optimum set of test cases are obtained by selection procedure which can be viewed as an optimization problem. So metaheuristic optimizing (searching) techniques have been immensely used to automate software testing task. The application of metaheuristic searching techniques in software testing is termed as Search Based Testing. Non-redundant, reliable and optimized test cases can be generated by the search based testing with less effort and time. This article presents a systematic review on several meta heuristic techniques like Genetic Algorithms, Particle Swarm optimization, Ant Colony Optimization, Bee Colony optimization, Cuckoo Searches, Tabu Searches and some modified version of these algorithms used for test case generation. The authors also provide one framework, showing the advantages, limitations and future scope or gap of these research works which will help in further research on these works.

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