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Bio-Inspired Computational Intelligence and Its Application to Software Testing

Bio-Inspired Computational Intelligence and Its Application to Software Testing

Abhishek Pandey, Soumya Banerjee
Copyright: © 2017 |Pages: 16
ISBN13: 9781522521280|ISBN10: 1522521283|EISBN13: 9781522521297
DOI: 10.4018/978-1-5225-2128-0.ch014
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MLA

Pandey, Abhishek, and Soumya Banerjee. "Bio-Inspired Computational Intelligence and Its Application to Software Testing." Handbook of Research on Soft Computing and Nature-Inspired Algorithms, edited by Shishir K. Shandilya, et al., IGI Global, 2017, pp. 429-444. https://doi.org/10.4018/978-1-5225-2128-0.ch014

APA

Pandey, A. & Banerjee, S. (2017). Bio-Inspired Computational Intelligence and Its Application to Software Testing. In S. Shandilya, S. Shandilya, K. Deep, & A. Nagar (Eds.), Handbook of Research on Soft Computing and Nature-Inspired Algorithms (pp. 429-444). IGI Global. https://doi.org/10.4018/978-1-5225-2128-0.ch014

Chicago

Pandey, Abhishek, and Soumya Banerjee. "Bio-Inspired Computational Intelligence and Its Application to Software Testing." In Handbook of Research on Soft Computing and Nature-Inspired Algorithms, edited by Shishir K. Shandilya, et al., 429-444. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-2128-0.ch014

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Abstract

Bio inspired algorithms are computational procedure inspired by the evolutionary process of nature and swarm intelligence to solve complex engineering problems. In the recent times it has gained much popularity in terms of applications to diverse engineering disciplines. Now a days bio inspired algorithms are also applied to optimize the software testing process. In this chapter authors will discuss some of the popular bio inspired algorithms and also gives the framework of application of these algorithms for software testing problems such as test case generation, test case selection, test case prioritization, test case minimization. Bio inspired computational algorithms includes genetic algorithm (GA), genetic programming (GP), evolutionary strategies (ES), evolutionary programming (EP) and differential evolution(DE) in the evolutionary algorithms category and Ant colony optimization(ACO), Particle swarm optimization(PSO), Artificial Bee Colony(ABC), Firefly algorithm(FA), Cuckoo search(CS), Bat algorithm(BA) etc. in the Swarm Intelligence category(SI).

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