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Generating the structural graph-based model from a program source-code using chaotic forrest optimization algorithm
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DOI：10.1111/exsy.13228.png)
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One of the most important and costly stages in software development is maintenance. Understanding the structure of software will make it easier to maintain it more efficiently. Clustering software modules is thought to be an effective reverse engineering technique for deriving structural models of software from source code. In software module clustering, the most essential objectives are to minimize connections between produced clusters, maximize internal connections within created clusters, and maximize clustering quality. Finding the appropriate software system clustering model is considered an NP-complete task. The previously proposed approaches' key limitations are their low success rate, low stability, and poor modularization quality. In this paper, for optimal clustering of software modules, Chaotic based heuristic method using a forest optimization algorithm is proposed. The impact of chaos theory on the performance of the other SFLA-GA and PSO-GA has also been investigated. The results show that using the logistic chaos approach improves the performance of these methods in the software-module clustering problem. The performance of chaotic based FOA, SFLA-GA and PSO-GA is superior to the other heuristic methods in terms of modularization quality and stability of the results.
Keyword:
forest optimization algorithm
modularization quality
module dependency graph
software modules clustering
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2.3
论文数:
2.6K
被引数:
3.8K
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