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software module clustering: An In-Depth Literature Analysis

IEEE TRANSACTIONS ON software ENGINEERING 2022年第6期48卷 1905-1928页

作者： Sarhan, Qusay, I Ahmed, Bestoun S. Bures, Miroslav Zamli, Kamal Z. Univ Duhok Dept Comp Sci Duhok 07006 Kurdistan Regio Iraq Karlstad Univ Dept Math & Comp Sci S-65188 Karlstad Sweden Czech Tech Univ Fac Elect Engn Dept Comp Sci Karlovo Nam 35 Prague 13121 2 Czech Republic Univ Malaysia Pahang Coll Comp & Appl Sci Fac Comp Pekan 26600 Malaysia

software module clustering is an unsupervised learning method used to cluster software entities (e.g., classes, modules, or files) with similar features. The obtained clusters may be used to study, analyze, and understand the software entities' structure and behavior. Implementing software module clustering with optimal results is challenging. Accordingly, researchers have addressed many aspects of software module clustering in the past decade. Thus, it is essential to present the research evidence that has been published in this area. In this study, 143 research papers from well-known literature databases that examined software module clustering were reviewed to extract useful data. The obtained data were then used to answer several research questions regarding state-of-the-art clustering approaches, applications of clustering in software engineering, clustering processes, clustering algorithms, and evaluation methods. Several research gaps and challenges in software module clustering are discussed in this paper to provide a useful reference for researchers in this field.

关键词： software Search problems Data mining software engineering software algorithms clustering algorithms Systematics Systematic literature study software module clustering clustering applications clustering algorithms clustering evaluation clustering challenges

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software module clustering Algorithm Using Probability Selection

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Wuhan University Journal of Natural Sciences 2018年第2期23卷 93-102页

作者： SUN Jiaze LING Beilei School of Computer Science and Technology Xi'anUniversity of Posts and Telecommunications Xi'an 710121 ShaanxiChina Shaanxi Key Laboratory of Network Data IntelligentProcessing Xi'an University of Posts and TelecommunicationsXi'an 710121 Shaanxi China

software module clustering problem is an important and challenging problem in software reverse engineering whose main goal is to obtain a good modular structure of the software system. The large complex software system can be divided into some subsystems that are easy to understand and maintain through the software module clustering. Aiming at solving the problem of slow convergence speed, the poor clustering result, and the complex algorithm, a software module clustering algorithm using probability selection is proposed. Firstly, we convert the software system into complex network diagram, and then we use the operation of merger, adjustment and optimization to get the software module clustering scheme. To evaluate the effectiveness of the algorithm, a set of experiments was performed on 5 real-world module clustering problems. The comparison of the experimental results proves the simplicity of the algorithm as well as the low time complexity and fast convergence speed. This algorithm provides a simple and effective engineering method for software module clustering problem.

关键词： software module clustering complex network merger adjustment optimization probability selection

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software module clustering using a Hyper-heuristic based Multi-objective Genetic Algorithm

Software module clustering using a Hyper-heuristic based Mul...

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3rd IEEE International Advance Computing Conference (IACC)

作者： Kumari, A. Charan Srinivas, K. Gupta, M. P. Dayalbagh Educ Inst Dept Phys & Comp Sci Agra Uttar Pradesh India Indian Inst Technol Dept Management Studies New Delhi 110016 India

ISBN: (纸本)9781467345286

This paper presents a Fast Multi-objective Hyper-heuristic Genetic Algorithm (MHypGA) for the solution of Multi-objective software module clustering Problem. Multi-objective software module clustering Problem is an important and challenging problem in software Engineering whose main goal is to obtain a good modular structure of the software System. software Engineers greatly emphasize on good modular structure as it is easier to comprehend, develop and maintain such software systems. In recent times, the problem has been converted into a Search-based software Engineering Problem with multiple objectives. This problem is NP hard as it is an instance of Graph Partitioning and hence cannot be solved using traditional optimization techniques. The MHypGA is a fast and effective metaheuristic search technique for suggesting software module clusters in a software system while maximizing cohesion and minimizing the coupling of the software modules. It incorporates twelve low-level heuristics which are based on different methods of selection, crossover and mutation operations of Genetic Algorithms. The selection mechanism to select a low-level heuristic is based on reinforcement learning with adaptive weights. The efficacy of the algorithm has been studied on six real-world module clustering problems reported in the literature and the comparison of the results prove the superiority of the MHypGA in terms of quality of solutions and computational time.

关键词： Search-based software Engineering software module clustering Multi-objective optimization Hyper-heuristics Reinforcement learning Genetic Algorithm

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software module clustering using Metaheuristic Search Techniques: A Survey 3

Software Module Clustering using Metaheuristic Search Techni...

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3rd International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Singh, Vineeta Northcap Univ Gurgaon India

ISBN: (纸本)9789380544199

This paper presents a literature survey on different metaheuristic search techniques that have been applied to software module clustering problem in the software maintenance phase of the software development life cycle. software maintenance is the process of improving and optimizing software. It involves alterations to the software in order to rectify defects and deficiencies found during field usage as well as the inclusion of new functionality to enhance the software's usability. This is one of the main areas where search-based approaches were reported in the literature.

关键词： Metaheuristicsearch techniques search based software engineering software module clustering

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A multi-agent evolutionary algorithm for software module clustering problems

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SOFT COMPUTING 2017年第12期21卷 3415-3428页

作者： Huang, Jinhuang Liu, Jing Yao, Xin Xidian Univ Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China Univ Birmingham CERCIA Sch Comp Sci Birmingham B15 2TT W Midlands England

The aim of software module clustering problems (SMCPs) is to automatically find a good quality clustering of software modules based on relationships among modules. In this paper, we propose a multi-agent evolutionary algorithm to solve this problem, labeled as MAEA-SMCPs. With the intrinsic properties of SMCPs in mind, three evolutionary operators are designed for agents to realize the purpose of competition, cooperation, and self-learning. In the experiments, practical problems are used to validate the performance of MAEA-SMCPs. The results show that MAEA-SMCPs can find clusters with high quality and small deviations. The comparison results also show that MAEA-SMCPs outperforms two existing multi-objective algorithms, namely MCA and ECA, and two existing single-objective algorithms, namely GGA and GNE, in terms of MQ.

关键词： software module clustering Multi-agent evolutionary algorithm Modularization quality

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FP-ABC: Fuzzy-Pareto dominance driven artificial bee colony algorithm for many-objective software module clustering

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COMPUTER LANGUAGES SYSTEMS & STRUCTURES 2018年第Jan.期51卷 1-21页

作者： Amarjeet Chhabra, Jitender Kumar NIT Kurukshetra Dept Comp Engn Kurukshetra Haryana India

Recently, multi-objective evolutionary algorithms (MOEAs) gained wide attention to solve various search-based software engineering (SBSE) problems. The formulation of software module clustering problem (SMCP) as SBSE problem facilitates the application of many MOEAs. Even though MOEAs are being used successfully to solve the SMCPs, the performance of Pareto-dominance based MOEAs degrade if the number of objective functions is greater than three. To address the large and complex many-objective SMCPs (MaSMCPs), changes in MOEAs are essential. This paper proposes a Fuzzy-Pareto dominance driven Artificial bee colony (FP-ABC) to solve the MaSMCPs effectively and efficiently. In this contribution, fuzzy-Pareto dominance and two external archive concepts have been integrated into artificial bee colony (ABC) algorithm. The fuzzy-Pareto dominance improves the selection process of candidate solution and two external archives concept helps in balancing the convergence and diversity. To validate the supremacy of the proposed approach, a comparative study is performed with the existing many-objective optimization algorithms such as Two-Arch2, NSGA-III, MOEA/D, and IBEA, over seven real-world problems. The statistical analysis of the results indicates that the proposed approach outperforms existing approaches in terms of modularization quality (MQ), coupling, cohesion, and Inverted Generational Distance (IGD). (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Artificial bee colony Many-objective optimization software module clustering

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Density PSO-based software module clustering algorithm

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The Journal of China Universities of Posts and Telecommunications 2018年第4期25卷 38-47页

作者： Sun Jiaze Ling Beilei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Intelligent Processing Xi'an University of Posts and Telecommunications

software module clustering is to divide the complex software system into many subsystems to enhance the intelligibility and maintainability of software systems. To increase convergence speed and optimize clustering solution,density PSO-based( DPSO) software module clustering algorithm is proposed. Firstly,the software system is converted into complex network diagram,and then the particle swarm optimization( PSO) algorithm is *** shortest path method is used to initialize the swarm,and the probability selection approach is used to update the particle positions. Furthermore,density-based modularization quality( DMQ) function is designed to evaluate the clustering quality. Five typical open source projects are selected as benchmark programs to verify the efficiency of the DPSO algorithm. Hill climbing( HC) algorithm,genetic algorithm( GA),PSO and DPSO algorithm are compared in the modularization quality( MQ) and DMQ. The experimental results show that the DPSO is more stable and more convergent than the other three traditional algorithms. The DMQ standard is more reasonable than MQ standard in guiding software module clustering.

关键词： software module clustering complex network PSO MQ modularity density

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

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DATA TECHNOLOGIES AND APPLICATIONS 2021年第2期55卷 251-279页

作者： Arasteh, Bahman Sadegi, Razieh Arasteh, Keyvan Islamic Azad Univ Tabriz Branch Dept Comp Engn Tabriz Iran

Purpose - software module clustering is one of the reverse engineering techniques, which is considered to be an effective technique for presenting software architecture and structural information. The objective of clustering software modules is to achieve minimum coupling among different clusters and create maximum cohesion among the modules of each cluster. Finding the best clustering is considered to be a multi-objective N-P hard optimization-problem, and for solving this problem, different meta-heuristic algorithms have been previously proposed. Achieving higher module lustering quality (MQ), obtaining higher success rate for achieving the best clustering quality and improving convergence speed are the main objectives of this study. Design/methodology/approach - In this study, a method (Bolen) is proposed for clustering software modules which combines the two algorithms of shuffled frog leaping and genetic algorithm. Findings - The results of conducted experiments using traditional data sets confirm that the proposed method outperforms the previous methods in terms of convergence speed, module clustering quality and stability of the results. Originality/value - The study proposes SFLA_GA algorithm for optimizing software module clustering, implementing SFLA algorithm in a discrete form by two operators of the genetic algorithm and achieving the above-mentioned purposes in this study. The aim is to achieve higher performance of the proposed algorithm in comparison with other algorithms.

关键词： software maintenance software module clustering clustering quality Shuffled frog leaping and genetic algorithms

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Large Neighborhood Search applied to the software module clustering problem

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COMPUTERS & OPERATIONS RESEARCH 2018年 91卷 92-111页

作者： Moncores, Marlon C. Alvim, Adriana C. F. Barros, Marcio O. UNIRIO Univ Fed Estado Rio de Janeiro Ctr Ciencias Exatas & Tecnol Av Pasteur 458 BR-22290240 Rio De Janeiro RJ Brazil Fed Univ State Rio de Janeiro Postgrad Informat Syst Program Rio De Janeiro Brazil

The software module clustering problem seeks to distribute the modules comprising a software system into clusters to maximize cluster cohesion and minimize coupling between clusters. Metaheuristics based on local search have been successfully applied to find good solutions for this problem, outperforming more-complex, population-based heuristics. In this paper, we present a heuristic based on Large Neighborhood Search to address the software module clustering problem. We also perform what is to our knowledge the largest experimental study addressing this problem to date, involving 124 instances of varying size and complexity and comparing our proposed algorithm to the heuristic that has found the best results for the problem so far. Our proposed algorithm outperformed the state-of-the-art heuristic on 93 out of 124 instances with 95% confidence level. We also report new upper bounds on the MQ value for 44 instances and evaluated the relative goodness of the solutions obtained by our proposed algorithm for 89 instances. Considering the 77 instances for which optimal solutions are proven, the proposed algorithm found the optimal solution for 30 instances (39%). Additionally, thirteen developers participate in a study focused on the distribution of the modules comprising a software project into clusters. We used JodaMoney as our study object and we compared the characteristics of the solutions generated by its authors, the best solution generated by LNS_SMC and the solution generated by each of the 13 subjects. LNS_SMC solution performs better than inexperienced subjects ones in issue resolution prediction but performs worse than the solutions proposed by experienced subjects and also the author's solution. For the prediction of concomitant changes, the LNS_SMC solution was outperformed by both subjects and authors solutions. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： SBSE software module clustering Large Neighborhood Search

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Hyper-heuristic approach for multi-objective software module clustering

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JOURNAL OF SYSTEMS AND software 2016年 117卷 384-401页

作者： Kumari, A. Charan Srinivas, K. NORTHCAP Univ Dept Comp Sci & Engn Gurgaon India Dayalbagh Educ Inst Dept Elect Engn Fac Engn Agra Uttar Pradesh India

In the software maintenance phase of software development life cycle, one of the main concerns of software engineers is to group the modules into clusters with maximum cohesion and minimum coupling. To analyze the efficacy of Multi-objective Hyper-heuristic Evolutionary Algorithm (MHypEA) in solving real-world clustering problems and to compare the results with the reported results in the literature for single as well as multi-objective formulations of the problem and also to present a CASE tool that assists software engineers in software module clustering process. The paper reports on empirical evaluation of the performance of MHypEA with the reported results in the literature. The comparison is mainly based on two factors - quality of the obtained solutions and the computational effort. On all the attempted problems, MHypEA reported good results in comparison to all the studies that were reported on multi-objective formulation of the problem, with a computational effort of nearly one twentieth of the computational effort required by the other multi-objective algorithms. The hyper-heuristic approach is able to produce high quality clustered systems with less computational effort. (C) 2016 Elsevier Inc. All rights reserved.

关键词： software maintenance software module clustering Multi-objective optimization Hyper-heuristics

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