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Software Module Clustering Using Bio-Inspired Algorithms

Software Module Clustering Using Bio-Inspired Algorithms

Kawal Jeet, Renu Dhir
Copyright: © 2016 |Pages: 26
ISBN13: 9781466696440|ISBN10: 1466696443|EISBN13: 9781466696457
DOI: 10.4018/978-1-4666-9644-0.ch017
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MLA

Jeet, Kawal, and Renu Dhir. "Software Module Clustering Using Bio-Inspired Algorithms." Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics, edited by Pandian Vasant, et al., IGI Global, 2016, pp. 445-470. https://doi.org/10.4018/978-1-4666-9644-0.ch017

APA

Jeet, K. & Dhir, R. (2016). Software Module Clustering Using Bio-Inspired Algorithms. In P. Vasant, G. Weber, & V. Dieu (Eds.), Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics (pp. 445-470). IGI Global. https://doi.org/10.4018/978-1-4666-9644-0.ch017

Chicago

Jeet, Kawal, and Renu Dhir. "Software Module Clustering Using Bio-Inspired Algorithms." In Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics, edited by Pandian Vasant, Gerhard-Wilhelm Weber, and Vo Ngoc Dieu, 445-470. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-9644-0.ch017

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Abstract

Nature has always been a source of inspiration for human beings. Large numbers of complex optimization problems have been solved by the techniques inspired by nature. Software modularization is one of such complex problems that have been encountered by software engineers. It is the process of organizing modules of a software system into optimal clusters. In this chapter, some bio-inspired algorithms such as bat, artificial bee colony, black hole and firefly algorithm have been proposed for the cause of software modularization. The hybrid of these algorithms with crossover and mutation operators of the genetic algorithm has also been proposed. All the algorithms along with their hybrids are tested on seven benchmark open source software systems. It has been evaluated from the results thus obtained that the hybrid of these algorithms proved to optimize better than the existing genetic and hill-climbing approaches.

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