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Software Module Clustering Using Bio-Inspired Algorithms

Software Module Clustering Using Bio-Inspired Algorithms

Kawal Jeet, Renu Dhir
Copyright: © 2021 |Pages: 26
ISBN13: 9781799830160|ISBN10: 1799830160|EISBN13: 9781799830177
DOI: 10.4018/978-1-7998-3016-0.ch035
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MLA

Jeet, Kawal, and Renu Dhir. "Software Module Clustering Using Bio-Inspired Algorithms." Research Anthology on Recent Trends, Tools, and Implications of Computer Programming, edited by Information Resources Management Association, IGI Global, 2021, pp. 788-813. https://doi.org/10.4018/978-1-7998-3016-0.ch035

APA

Jeet, K. & Dhir, R. (2021). Software Module Clustering Using Bio-Inspired Algorithms. In I. Management Association (Ed.), Research Anthology on Recent Trends, Tools, and Implications of Computer Programming (pp. 788-813). IGI Global. https://doi.org/10.4018/978-1-7998-3016-0.ch035

Chicago

Jeet, Kawal, and Renu Dhir. "Software Module Clustering Using Bio-Inspired Algorithms." In Research Anthology on Recent Trends, Tools, and Implications of Computer Programming, edited by Information Resources Management Association, 788-813. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-3016-0.ch035

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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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