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Novel Automatic Approach Using Modified Differential Evaluation to Software Module Clustering Problem

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Abstract

Software modularization is complex but crucial to the software design process. The complexity in software modularization arises due to inter-dependencies among its constituting elements. A good software structure performs certain enhancement, and makes maintenance work easier for developers that consequently saves development and testing effort cycles. Software failures due to poor design and structure have motivated exhaustive research in this field. This encouraged the formulation of software modularization clustering problem (SMCP) as a search problem with an aim to identify the possible best module clustering solution that can optimize cohesion and coupling. With the inception of search-based software engineering, many optimization problems have been successfully addressed with meta-heuristics. Differential evolution (DE), a popular algorithm, is stable, versatile, robust with few control parameters and considered as an effective meta-heuristic for solving optimization problems. This paper introduces a novel formulation of DE as differential evolution software module clustering (DESMC) to identify optimal clustering solution for any software. It is guided by fitness based on modularization quality factors. The proposed formulation redefines classical concepts of donor vector, trial vector, and mutation operator for DESMC to adapt the discrete nature of SMCP. The performance of DESMC is evaluated on a set of test problems identified from literature and open source. Further, the results are compared against other three rivalry approaches, namely hill climbing, genetic algorithm, and particle swarm optimization. The observation shows that DESMC outperforms the existing state-of-the-art methods.

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Acknowledgements

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Authors and Affiliations

  1. Amity University Uttar Pradesh, Noida, India

    Neetu Gupta & Sumit Kumar

  2. NSUT East Campus, Geeta Colony, Delhi, India

    Vishal Gupta

  3. School of Engineering and Technology, Sharda University, Greater Noida, India

    Surbhi Vijh

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  1. Neetu Gupta
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  2. Sumit Kumar
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Correspondence to Sumit Kumar.

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This article is part of the topical collection “Advanced Computing and Data Sciences” guest edited by Mayank Singh, Vipin Tyagi and P.K. Gupta.

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Gupta, N., Kumar, S., Gupta, V. et al. Novel Automatic Approach Using Modified Differential Evaluation to Software Module Clustering Problem. SN COMPUT. SCI. 4, 816 (2023). https://doi.org/10.1007/s42979-023-02238-6

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