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MOPISDE: A collaborative multi-objective information-sharing DE algorithm for software clustering
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AI
DOI：10.1016/j.eswa.2023.120207.png)
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Abstract
En 中文
The software module clustering problem (SMCP) aims to improve the internal quality of software while software engineers understand the system architecture and facilitating software system maintenance. However, most current methods ignore modular stability in software evolution and the topological properties software architecture and hence obtain decompositions much worse than the expert. Therefore, we propose collaborative multi-objective information-sharing differential evolution (MOPISDE) algorithm for SMCP global stability and path complexity as two new objective functions. Specifically, two new concepts are for SMCP as two objectives of populations and modular quality (MQ) as an objective of the third population. Population-sharing technology is designed to collaboratively exchange information among different populations to address the lack of diversity in a single population. An information-sharing three-stage differential evolution strategy is presented to reduce the search space and improve search performance by sharing good substructures among elite individuals. New mutation strategies are proposed to utilize the different substructures two random individuals as a new community to further improve the search performance. Experiments various projects demonstrate the superiority of the proposed algorithm. The proposed method not only convergence but also provides stable and accurate modularity that is somewhat closer to expert decomposition than that of other methods.
Keywords:
Software modularization
Multi-objective optimization
Differential evolution algorithm
Software architecture
Software understanding
Software system maintenance
Journal
IF:
7.5
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2.9W
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10.2W