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Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Metaheuristic Techniques for Test Case Generation: A Review

Metaheuristic Techniques for Test Case Generation: A Review
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Author(s): Rashmi Rekha Sahoo (ITER College, Siksha ‘O' Anusandhan University, Bhubaneswar, India)and Mitrabinda Ray (ITER College, Siksha ‘O' Anusandhan University, Bhubaneswar, India)
Copyright: 2022
Pages: 16
Source title: Research Anthology on Agile Software, Software Development, and Testing
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-3702-5.ch052


View Metaheuristic Techniques for Test Case Generation: A Review on the publisher's website for pricing and purchasing information.


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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