A Modified Horse Herd Optimization Algorithm and Its Application in the Program Source Code Clustering

Bahman Arasteh; Peri Gunes; Asgarali Bouyer; Farhad Soleimanian Gharehchopogh; Hamed Alipour Banaei; Reza Ghanbarzadeh

doi:10.1155/2023/3988288

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A Modified Horse Herd Optimization Algorithm and Its Application in the Program Source Code Clustering

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A Modified Horse Herd Optimization Algorithm and Its Application in the Program Source Code Clustering

Bahman Arasteh, Peri Gunes, Asgarali Bouyer, Farhad Soleimanian Gharehchopogh, Hamed Alipour Banaei and Reza Ghanbarzadeh

Complexity (New York, N.Y.), Vol.2023, 3988288

27/12/2023

DOI: https://doi.org/10.1155/2023/3988288

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Abstract

Maintenance is one of the costliest phases in the software development process. If architectural design models are accessible, software maintenance can be made more straightforward. When the software’s source code is the only available resource, comprehending the program profoundly impacts the costs associated with software maintenance. The primary objective of comprehending the source code is extracting information used during the software maintenance phase. Generating a structural model based on the program source code is an effective way of reducing overall software maintenance costs. Software module clustering is considered a tremendous reverse engineering technique for constructing structural design models from the program source code. The main objectives of clustering modules are to reduce the quantity of connections between clusters, increase connections within clusters, and improve the quality of clustering. Finding the perfect clustering model is considered an NP-complete problem, and many previous approaches had significant issues in addressing this problem, such as low success rates, instability, and poor modularization quality. This paper applied the horse herd optimization algorithm, a distinctive population-based and discrete metaheuristic technique, in clustering software modules. The proposed method’s effectiveness in addressing the module clustering problem was examined by ten real-world standard software test benchmarks. Based on the experimental data, the quality of the clustered models produced is approximately 3.219, with a standard deviation of 0.0718 across the ten benchmarks. The proposed method surpasses former methods in convergence, modularization quality, and result stability. Furthermore, the experimental results demonstrate the versatility of this approach in effectively addressing various real-world discrete optimization challenges.

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Title: A Modified Horse Herd Optimization Algorithm and Its Application in the Program Source Code Clustering
Creators: Bahman Arasteh - Istinye University
Peri Gunes - Istanbul Aydın University
Asgarali Bouyer - Istinye University
Farhad Soleimanian Gharehchopogh - Islamic Azad University of Urmia
Hamed Alipour Banaei - Islamic Azad University of Tabriz
Reza Ghanbarzadeh - Southern Cross University
Contributors: Roberto Natella (Editor)
Publication Details: Complexity (New York, N.Y.), Vol.2023, 3988288
Publisher: John Wiley & Sons, Inc.
Identifiers: 991013157412702368
Copyright: Copyright © 2023 Bahman Arasteh et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Academic Unit: Information Technology; Faculty of Science and Engineering
Language: English
Resource Type: Journal article

A Modified Horse Herd Optimization Algorithm and Its Application in the Program Source Code Clustering

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