Bölen: software module clustering method using the combination of shuffled frog leaping and genetic algorithm

Arasteh, Bahman; Sadegi, Razieh; Arasteh, Keyvan

doi:10.1108/dta-08-2019-0138

Cited by 26 publications

(11 citation statements)

References 24 publications

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“…In this subsection, the proposed clustering method was compared with GA-based module clustering, 14 PSO-based module clustering, 26 and SFLA-based module clustering 21 algorithms which have been presented in Arasteh et al 21 The implementation details (programing language, data structures, and algorithms) of the nondeterministic algorithms (GA, PSO, and SFLA) have impact on their behavior. Different implementations of a clustering algorithm may generate different clusters with different MQ for a data set.…”

Section: Resultsmentioning

confidence: 99%

“…Chhabra 20 Improving modular structure of software system using structural and lexical dependency NSGA-II Multi-objective Chhabra 13 Two-Archive Artificial Bee Colony for Multi-objective Software Module Clustering Problem TA-ABC Multi-objective Arasteh et al 21 Bölen: software module clustering method using the combination of shuffled frog leaping and genetic algorithm, Data Technologies and Applications SFLA-GA Single-objective Arasteh et al 22 ARAZ: A software modules clustering method using the combination of particle swarm optimization and genetic algorithms PSO-GA Single-objective Chhabra 13 A graph-based clustering algorithm for software systems modularization TA-ABC Single-objective entities and relations among them in a file. Then, according to the information obtained from the file, the intended MDG can be designed.…”

Section: Nsga-ii Multi-objectivementioning

confidence: 99%

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Savalan: Multi objective and homogeneous method for software modules clustering

Arasteh

Fatolahzadeh

Kiani

2021

J Software Evolu Process

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Reverse engineering is used for extracting and understanding software architecture models from source code when the source code is the only available software product. Software module clustering is a reverse engineering method which decomposes software modules into several clusters (subsystems) by using module dependency graph. Finding the best clusters for the modules of software is a multi-objective and NP-hard problem; maximizing the cohesion among the modules, minimizing the coupling among different clusters, and maximizing the software modularization quality are considered as the main objectives of software module clustering. Some of these objectives, such as cohesion and coupling, are in contradiction with each other.Simultaneous improvement of all clustering objectives (cohesion, coupling, modularization quality, size, and number of clusters) is the main goal of this study. In this paper, by capitalizing on multi objective genetic algorithm and a new combination of objective functions, we proposed a homogeneous method, namely, Savalan, for clustering software modules. The proposed method generates high-quality clusters with strong cohesion within clusters and weak connections between clusters for the input source code. The results of conducted experiments on the 14 benchmark programs indicate that simultaneous improvement of all clustering objectives is the main merit of this method. According to the experimental results, the proposed algorithm was able to outperform the previous multi objective methods.

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Section: Resultsmentioning

confidence: 99%

Section: Nsga-ii Multi-objectivementioning

confidence: 99%

Savalan: Multi objective and homogeneous method for software modules clustering

Arasteh

Fatolahzadeh

Kiani

2021

J Software Evolu Process

Self Cite

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“…All three solutions may solve the problem of SMC in terms of stability. (Arasteh et al, 2021) proposes (Bölen) a method for clustering software modules that combines the two methods of shuffled frog leaping (SFLA) and GA. The goal of this combination strategy was to improve clustering quality by attaining faster data convergence to the ideal response, greater software clustering quality (MQ) values, more data stability, and a higher success rate in getting the best MQ value.…”

Section: Related Workmentioning

confidence: 99%

“…For solving the SMC problem, several heuristic approaches have been developed. Some of the proposed methods in this study domain include the hill‐climbing algorithm (HC), genetic algorithm (GA), particle swarm optimization algorithm (PSO), and multi‐agent evolutionary algorithms (Arasteh et al, 2021; Bavota et al, 2012; Harman et al, 2005; Mahdavi et al, 2003; Maletic & Marcus, 2001; Mancoridis et al, 1999; Mitchell & Mancoridis, 2002; Pourasghar et al, 2021; Praditwong et al, 2011; Roger, 2000). Lower modularization quality (MQ), insufficient stability of the obtained results by the heuristic algorithms are the main drawbacks of the previous methods.…”

Section: Introductionmentioning

confidence: 99%

Generating the structural graph‐based model from a program source‐code using chaotic forrest optimization algorithm

et al. 2023

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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.

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“…In this way, the heuristic algorithm becomes a good choice. Although a large number of heuristic methods have been proposed to solve various optimization problems (Zhou et al , 2018, 2020; Arasteh et al , 2020; Shomali and Arasteh, 2020), as far as we know, no heuristic algorithms exist to solve the PSCP. Therefore, the purpose of this work is to enrich the repertory of solution methods for PSCP by presenting a novel Argentine ant system (AAS) algorithm.…”

Section: Introductionmentioning

confidence: 99%

An Argentine ant system algorithm for partial set covering problem

Liu

Zhou

Yin

et al. 2022

DTA

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PurposeThe paper aims to provide an efficient meta-heuristic algorithm to solve the partial set covering problem (PSCP). With rich application scenarios, the PSCP is a fascinating and well-known non-deterministic polynomial (NP)-hard problem whose goal is to cover at least k elements with as few subsets as possible.Design/methodology/approachIn this work, the authors present a novel variant of the ant colony optimization (ACO) algorithm, called Argentine ant system (AAS), to deal with the PSCP. The developed AAS is an integrated system of different populations that use the same pheromone to communicate. Moreover, an effective local search framework with the relaxed configuration checking (RCC) and the volatilization-fixed weight mechanism is proposed to improve the exploitation of the algorithm.FindingsA detailed experimental evaluation of 75 instances reveals that the proposed algorithm outperforms the competitors in terms of the quality of the optimal solutions. Also, the performance of AAS gradually improves with the growing instance size, which shows the potential in handling complex practical scenarios. Finally, the designed components of AAS are experimentally proved to be beneficial to the whole framework. Finally, the key components in AAS have been demonstrated.Originality/valueAt present, there is no heuristic method to solve this problem. The authors present the first implementation of heuristic algorithm for solving PSCP and provide competitive solutions.

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Bölen: software module clustering method using the combination of shuffled frog leaping and genetic algorithm

Cited by 26 publications

References 24 publications

Savalan: Multi objective and homogeneous method for software modules clustering

Savalan: Multi objective and homogeneous method for software modules clustering

Generating the structural graph‐based model from a program source‐code using chaotic forrest optimization algorithm

An Argentine ant system algorithm for partial set covering problem

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