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Many-objective artificial bee colony algorithm for large-scale software module clustering problem

SOFT COMPUTING 2018年第19期22卷 6341-6361页

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

The meta-heuristic search algorithms have been widely applied to solve the various science and engineering optimization problems. However, the performance of these algorithms is highly sensitive toward the number of objective functions and number of decision variables. Recently, it has been explored by the researchers that the performance of such algorithm degrades when the number of objective functions and decision variables increases by some limit. Hence, these algorithms can be hardly acceptable to the real-world optimization problems such as software module clustering problem (SMCP), which contains a large number of objective functions and decision variables. Previous researchers have proposed several approaches to address the many-objective optimization problems by revising existing meta-heuristic algorithms. Recently, an artificial bee colony algorithm (ABC), a meta-heuristic algorithm, effectively used to address the several multi-objective optimization problems. Even though in most of the cases ABC algorithm performs better compared to other meta-heuristic algorithms, it faces the same problems as other meta-heuristic algorithms for a large number of objective functions and decision variables. This paper proposes a many-objective artificial bee colony (MaABC) algorithm to solve many-objective SMCPs. In this contribution, we revised the original ABC by using, quality indicator, -norm-based (p < 1) distances, and two external archives concepts. To validate the proposed approach, an extensive comparative study is performed with the existing many-objective optimization algorithms (i.e., Two-Arch2, NSGA-III, MOEA/D, and IBEA) over seven SMCPs. The statistical analysis of the results show that the proposed MaABC outperforms existing many-objective approaches in terms of modularization quality (MQ),cohesion, coupling, and inverted generational distance (IGD).

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

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TA-ABC: Two-Archive Artificial Bee Colony for Multi-objective software module clustering Problem

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JOURNAL OF INTELLIGENT SYSTEMS 2018年第4期27卷 619-641页

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

Multi-objective software module clustering problem (M-SMCP) aims to automatically produce clustering solutions that optimize multiple conflicting clustering criteria simultaneously. Multi-objective evolutionary algorithms (MOEAs) have been a most appropriate alternate for solving M-SMCPs. Recently, it has been observed that the performance of MOEAs based on Pareto dominance selection technique degrades with multi-objective optimization problem having more than three objective functions. To alleviate this issue for M-SMCPs containing more than three objective functions, we propose a two-archive based artificial bee colony (TA-ABC) algorithm. For this contribution, a two-archive concept has been incorporated in the TA-ABC algorithm. Additionally, an improved indicator-based selection method is used instead of Pareto dominance selection technique. To validate the performance of TA-ABC, an empirical study is conducted with two well-known M-SMCPs, i.e. equal-size cluster approach and maximizing cluster approach, each containing five objective functions. The clustering result produced by TA-ABC is compared with existing genetic based two-archive algorithm (TAA) and non-dominated sorting genetic algorithm II (NSGA-II) over seven un-weighted and 10 weighted practical problems. The comparison results show that the proposed TA-ABC outperforms significantly TAA and NSGA-II in terms of modularization quality, coupling, cohesion, Pareto optimality, inverted generational distance, hypervolume, and spread performance metrics.

关键词： Artificial bee colony multi-objective optimization software module clustering two-archive algorithm

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A Particle Swarm Optimization-Based Heuristic for software module clustering Problem

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2018年第12期43卷 7083-7094页

作者： Prajapati, Amarjeet Chhabra, Jitender Kumar JIIT Dept Comp Sci & IT Noida Uttar Pradesh India NIT Kurukshetra Dept Comp Engn Kurukshetra Haryana India

The large-scale software module clustering problems (SMCPs) are very difficult to solve by using traditional analytical/deterministic-based optimization methods due to their high complexity and computation cost. Recently, particle swarm optimization (PSO) algorithm, a non-deterministic meta-heuristic search algorithm, gained wide attention and has been adapted to address the various large-scale science and engineering optimization problems. However, the applicability and usefulness of PSO algorithm have not been studied by any researcher till date to solve the SMCPs. In this paper, we introduce PSO-based module clustering (PSOMC), which partitions software system by optimizing: (1) intracluster dependency, (2) intercluster dependency, (3) a number of clusters, and (4) a number of module per cluster. To this contribution, we redefine the terms position and velocity of original PSO under the discrete scenario that best suited to SMCPs. To demonstrate the performance of the proposed approach, extensive experiments on six real-world SMCPs are carried out. We also compare our approach with existing state-of-the-art software module clustering meta-heuristic approaches (group genetic algorithm, hill climbing, and simulated annealing algorithm). The experimental results show that the proposed approach is effective and promising for solving SMCPs.

关键词： software module clustering Particle swarm optimization software restructuring

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An efficient heuristic algorithm for software module clustering optimization

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JOURNAL OF SYSTEMS AND software 2022年 190卷

作者： Yuste, Javier Duarte, Abraham Pardo, Eduardo G. Univ Rey Juan Carlos C-Tulipan S-N Madrid 28933 Spain

In the lifecycle of software projects, maintenance tasks usually entail 75% of the total costs, where most efforts are spent in understanding the program. To improve the maintainability of software projects, the code is often divided into components, which are then grouped in different modules following good design principles, lowering coupling and increasing cohesion. The software module clustering Problem (SMCP) is an optimization problem that looks for maximizing the modularity of software projects in the context of Search-Based software Engineering. In the SMCP, projects are often modeled as graphs. Therefore, the SMCP can be interpreted as a graph partitioning problem, which is proved to be NP-hard. In this work, we propose a new heuristic algorithm for software modularization, based on a Greedy Randomized Adaptive Search Procedure with Variable Neighborhood Descent. We present a three-fold categorization of neighborhoods for the SMCP and leverage domain-specific information to filter unpromising solutions. Our proposal has been successfully tested over a dataset of real software projects, outperforming the previous state-of-the-art approach in terms of Modularization Quality in very short computing times. Therefore, it could be integrated in software development tools to improve the quality of software projects in real time. (C)& nbsp;2022 Elsevier Inc. All rights reserved.

关键词： software module clustering Search-Based software Engineering Heuristics Modularization Quality Maintainability

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Parallelization of genetic algorithms for software architecture recovery

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AUTOMATED software ENGINEERING 2025年第1期32卷 1-24页

作者： Varol, Taha Elyasi, Milad Aktas, T. Huzeyfe Ozener, O. Orsan Sozer, Hasan Ozyegin Univ TR-34794 Istanbul Turkiye

software Architecture Recovery (SAR) techniques analyze dependencies between software modules and automatically cluster them to achieve high modularity. Many of these approaches employ Genetic Algorithms (GAs) for clustering software modules. A major drawback of these algorithms is their lack of scalability. In this paper, we address this drawback by introducing generic software components that can encapsulate subroutines (operators) of a GA to execute them in parallel. We use these components to implement a novel hybrid GA for SAR that exploits parallelism to find better solutions faster. We compare the effectiveness of parallel algorithms with respect to the sequential counterparts that are previously proposed for SAR. We observe that parallelization enables a greater number of iterations to be performed in the search for high-quality solutions. The increased efficiency achieved through parallel processing allows for faster convergence towards optimal solutions by harnessing the power of multiple processing units in a coordinated manner. The amount of improvement in modularity is above 50%, which particularly increases in the context of large-scale systems. Our algorithm can scale to recover the architecture of a large system, Chromium, which has more than 18,500 modules and 750,000 dependencies among these modules.

关键词： software architecture recovery software module clustering software modularity Genetic algorithms Parallel algorithms

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An experimental evaluation of the importance of randomness in hill climbing searches applied to software engineering problems

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EMPIRICAL software ENGINEERING 2014年第5期19卷 1423-1465页

作者： Barros, Marcio de O. PPGI UNIRIO Postgrad Informat Syst Program Rio De Janeiro RJ Brazil

Random number generators are a core component of heuristic search algorithms. They are used to build candidate solutions and reduce bias while transforming these solutions during the search. Despite their usefulness, random numbers also have drawbacks, as one cannot guarantee that all portions of the search space are covered by the search and must run an algorithm many times to statistically assess its behavior. Determine whether deterministic quasi-random sequences can be used as an alternative to pseudo-random numbers in feeding "randomness" into Hill Climbing searches addressing software Engineering problems. We have designed and executed three experimental studies in which a Hill Climbing search was used to find solutions for two software Engineering problems: software module clustering and requirement selection. The algorithm was executed using both pseudo-random numbers and three distinct quasi-random sequences (Faure, Halton, and Sobol). The software clustering problem was evaluated for 32 real-world instances and the requirement selection problem was addressed using 15 instances reused from previous research works. The experimental studies were chained to allow varying as few as possible experimental factors between any given study and its subsequent one. Results found by searches powered by distinct quasi-random sequences were compared to those produced by the pseudo-random search on a per instance basis. The comparison evaluated search efficiency (processing time required to run the search) and effectiveness (quality of results produced by the search). Contrary to previous findings observed in the context of other heuristic search algorithms, we found evidence that quasi-random sequences cannot outperform pseudo-random numbers regularly in Hill Climbing searches. Detailed statistical analysis is provided to support the evidence favoring pseudo-random numbers.

关键词： Pseudo-random number generation Quasi-random sequences Hill climbing software module clustering Next release problem

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Duzen: generating the structural model from the software source code using shuffled frog leaping algorithm

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NEURAL COMPUTING & APPLICATIONS 2023年第3期35卷 2487-2502页

作者： Arasteh, Bahman Karimi, Mohammad Bagher Sadegi, Razieh Istinye Univ Fac Engn & Nat Sci Dept Software Engn Istanbul Turkey Islamic Azad Univ Dept Comp Engn Tabriz Branch Tabriz Iran

The cost of software maintenance is heavily influenced by program understanding. When the source code is the only product accessible, maintainers spend a significant amount of effort trying to understand the structure and behavior of the software. Program module clustering is a useful reverse-engineering technique for obtaining the software structural model from source code. Finding the best clustering is regarded as an NP-hard optimization problem, and several meta-heuristic methods have been employed to solve it. The fundamental flaws of the prior approaches were their insufficient performance and effectiveness. The major goals of this research are to achieve improved software clustering quality and stability. A new method (Duzen) is proposed in this research for improving software module clustering. As a meta-heuristic memetic algorithm, this technique employs the shuffled frog-leaping algorithm. The Duzen results were investigated and compared to those produced using earlier approaches. In terms of obtaining the best clustering quality, the proposed method was shown to be better and more successful than the others;it also had higher data stability and data convergence to optimal replies in a fewer number of repetitions. Furthermore, it acquired a higher data mean and a faster clustering execution time.

关键词： software maintenance software module clustering clustering quality Shuffled frog leaping algorithm

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Genetic algorithms and heuristics hybridized for software architecture recovery

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AUTOMATED software ENGINEERING 2023年第2期30卷 1-24页

作者： Elyasi, Milad Simitcioglu, M. Esad Saydemir, Abdullah Ekici, Ali Ozener, O. Orsan Sozer, Hasan Ozyegin Univ Comp Sci Dept TR-34794 Istanbul Turkiye Ozyegin Univ Ind Engn Dept TR-34794 Istanbul Turkiye CNRS IMT Atlantique LS2N 4 Rue Alfred Kastler F-44300 Nantes France

Large scale software systems must be decomposed into modular units to reduce maintenance efforts. software Architecture Recovery (SAR) approaches have been introduced to analyze dependencies among software modules and automatically cluster them to achieve high modularity. These approaches employ various types of algorithms for clustering software modules. In this paper, we discuss design decisions and variations in existing genetic algorithms devised for SAR. We present a novel hybrid genetic algorithm that introduces three major differences with respect to these algorithms. First, it employs a greedy heuristic algorithm to automatically determine the number of clusters and enrich the initial population that is generated randomly. Second, it uses a different solution representation that facilitates an arithmetic crossover operator. Third, it is hybridized with a heuristic that improves solutions in each iteration. We present an empirical evaluation with seven real systems as experimental objects. We compare the effectiveness of our algorithm with respect to a baseline and state-of-the-art hybrid genetic algorithms. Our algorithm outperforms others in maximizing the modularity of the obtained clusters.

关键词： Reverse engineering software architecture recovery software module clustering software modularity Genetic algorithms

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ARAZ: A software modules clustering method using the combination of particle swarm optimization and genetic algorithms

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2020年第4期14卷 449-462页

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

A considerable percentage of software costs are usually related to its maintenance. Program comprehension is a prerequisite of the software maintenance and a considerable time of maintainers is spent to comprehend the structure and behavior of the software when the source code is the only product available. Program comprehension is one of difficult and challenging task especially in the absence of design documents of the software system. clustering of software modules is an effective reverse-engineering method for extracting the software architecture and structural model from the source code. Finding the best clustering is considered to be a multi-objective NP hard optimization-problem and different meta-heuristic algorithms have been used for solving this problem. Local optimum, insufficient quality, insufficient performance and insufficient stability are the main shortcomings of the previous methods. Attaining higher values for software clustering quality, attaining higher success rate in clustering of software modules, attaining higher stability of the obtained results and attaining the higher convergence (speed) to generate optimal clusters are the main goals of this study. In this study, a hybrid meta heuristic method (ARAZ) includes particle swarm optimization algorithm and genetic algorithm (PSO-GA) is proposed to find the best clustering of software modules. An extensive series of experiments on 10 standard benchmark programs have been conducted. Regarding the results of experiments, the proposed method outperforms the other methods in terms of clustering quality, stability, success rate and convergence speed.

关键词： software maintenance software module clustering particle swarm optimization algorithm clustering quality convergence speed

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Learning from optimization: A case study with Apache Ant

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INFORMATION AND software TECHNOLOGY 2015年第Jan.期57卷 684-704页

作者： Barros, Marcio de Oliveira Farzat, Fabio de Almeida Travassos, Guilherme Horta Univ Fed Estado Rio de Janeiro PPGI Postgrad Informat Syst Dept Rio de Janeiro RJ Brazil Univ Fed Rio de Janeiro COPPE Comp & Syst Engn Dept Rio de Janeiro RJ Brazil

Context: software architecture degrades when changes violating the design-time architectural intents are imposed on the software throughout its life cycle. Such phenomenon is called architecture erosion. When changes are not controlled, erosion makes maintenance harder and negatively affects software evolution. Objective: To study the effects of architecture erosion on a large software project and determine whether search-based module clustering might reduce the conceptual distance between the current architecture and the design-time one. Method: To run an exploratory study with Apache Ant. First, we characterize Ant's evolution in terms of size, change dispersion, cohesion, and coupling metrics, highlighting the potential introduction of architecture and code-level problems that might affect the cost of changing the system. Then, we reorganize the distribution of Ant's classes using a heuristic search approach, intending to re-emerge its design-time architecture. Results: In characterizing the system, we observed that its original, simple design was lost due to maintenance and the addition of new features. In optimizing its architecture, we found that current models used to drive search-based software module clustering produce complex designs, which maximize the characteristics driving optimization while producing class distributions that would hardly be acceptable to developers maintaining Ant. Conclusion: The structural perspective promoted by the coupling and cohesion metrics precludes observing the adequate software module clustering from the perspective of software engineers when considering a large open source system. Our analysis adds evidence to the criticism of the dogma of driving design towards high cohesion and low coupling, at the same time observing the need for better models to drive design decisions. Apart from that, we see SBSE as a learning tool, allowing researchers to test software Engineering models in extreme situations that would not be easil

关键词： Apache Ant Heuristic search software module clustering Experimental software Engineering

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