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39th Annual ACM Symposium on Applied Computing (SAC)

作者： Altinisik, Metin Sozer, Hasan Gursun, Gonca Turkcell Technol Istanbul Turkiye Ozyegin Univ Istanbul Turkiye Bosch Ctr Artificial Intelligence Renningen Germany

ISBN: (纸本)9798400702433

software architecture recovery tools cluster software modules based on their inter-dependencies to suggest a gross-level decomposition with high modularity. However, there are usually multiple types of dependencies among software modules, each of which can be captured with a separate dependency model. In this paper, we evaluate the effectiveness of a software architecture recovery approach that utilizes multiple such dependency models. First, we perform clustering based on various models separately. These models reflect different types of dependencies, including call dependencies, evolutionary coupling and dependencies of modules on commonly used database tables. Then, we perform cluster aggregation on the obtained clustering results to propose a packaging structure to the designer. We present an industrial case study on a large-scale legacy system from the telecommunications domain. The highest accuracy is achieved with the aggregation of clustering results obtained based on 3 dependency models. The accuracy in terms of matching between the proposed packaging and the existing one is above 65%. The non-matching results mostly pinpoint architectural anomalies as confirmed by domain experts.

关键词： reverse engineering software architecture recovery software module clustering software modularity cluster aggregation industrial case study

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Learning from optimization: A case study with Apache Ant

Learning from optimization: A case study with Apache Ant

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17th International Conference on Evaluation and Assessment in software Engineering

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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Variable Neighborhood Descent for software Quality Optimization 14th

Variable Neighborhood Descent for Software Quality Optimizat...

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14th International Conference on Metaheuristics (MIC)

作者： Yuste, Javier Pardo, Eduardo G. Duarte, Abraham Univ Rey Juan Carlos Madrid 28933 Spain

ISBN: (纸本)9783031265037;9783031265044

In the software Development Life-Cycle, the maintenance phase is often the most costly stage due to the efforts devoted to understanding the system. The software module clustering Problem (SMCP) is an optimization problem which objective is to find the most modular organization of software systems to ease their comprehension. In this problem, software projects are frequently modeled as graphs. Then, the objective of the SMCP, which is proved to be NP-hard, is to group the vertices in modules such that the modularity of the graph is maximized. In this work, we present an algorithm based on Variable Neighborhood Descent for the SMCP and study a novel quality metric, the Function of Complexity Balance, which was recently proposed for the problem. Our proposal has been favorably evaluated over a dataset of 34 real software projects, outperforming the previous state-of-the-art method, a Hybrid Genetic Algorithm, in terms of both quality and computing time. Furthermore, the results are statistically significant according to the Wilcoxon's signed rank test.

关键词： Search-based software engineering software module clustering Heuristics Function of complexity balance Maintainability

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HYGAR: A Hybrid Genetic Algorithm for software Architecture Recovery 22

HYGAR: A Hybrid Genetic Algorithm for Software Architecture ...

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37th Annual ACM Symposium on Applied Computing

作者： Elyasi, Milad Simitcioglu, Muhammed Esad Saydemir, Abdullah Ekici, Ali Sozer, Hasan Ozyegin Univ Istanbul Turkey

ISBN: (纸本)9781450387132

Genetic algorithms have been used for clustering modules of a software system in line with the modularity principle. The goal of these algorithms is to recover an architectural view in the form of a modular structural decomposition of the system. We discuss design decisions and variations in existing genetic algorithms devised for this purpose. We introduce HYGAR, a novel hybrid variant of existing algorithms. We apply HYGAR for software architecture recovery of 5 real systems and compare its effectiveness with respect to a baseline and a state-of-the-art hybrid algorithm. Results show that HYGAR outperforms these algorithms in maximizing the modularity of the obtained clustering.

关键词： reverse engineering software architecture recovery software module clustering software modularity genetic algorithms

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DCT: An Scalable Multi-Objective module clustering Tool 20

DCT: An Scalable Multi-Objective Module Clustering Tool

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20th IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM)

作者： Tarchetti, Ana Paula M. Amaral, Luis Oliveira, Marcos C. Bonifacio, Rodrigo Pinto, Gustavo Lo, David Univ Brasilia Brasilia DF Brazil Brazilian Minist Econ Brasilia DF Brazil Fed Univ Para Belem Para Brazil Singapore Management Univ Singapore Singapore

ISBN: (纸本)9781728192482

Maintaining complex software systems is a time-consuming and challenging task. Practitioners must have a general understanding of the system's decomposition and how the system's developers have implemented the software features (probably cutting across different modules). Re-engineering practices are imperative to tackle these challenges. Previous research has shown the benefits of using software module clustering (SMC) to aid developers during re-engineering tasks (e.g., revealing the architecture of the systems, identifying how the concerns are spread among the modules of the systems, recommending refactorings, and so on). Nonetheless, although the literature on software module clustering has substantially evolved in the last 20 years, there are just a few tools publicly available. Still, these available tools do not scale to large scenarios, in particular, when optimizing multi-objectives. In this paper we present the Draco clustering Tool (DCT), a new software module clustering tool. DCT design decisions make multi-objective software clusterization feasible, even for software systems comprising up to 1,000 modules. We report an empirical study that compares DCT with other available multi-objective tool (HD-NSGA-II), and both DCT and HD-NSGA-II with mono-objective tools (BUNCH and HD-LNS). We evidence that DCT solves the scalability issue when clustering medium size projects in a multi-objective mode. In a more extreme case, DCT was able to cluster Druid (an analytics data store) 221 times faster than HD-NSGA-II.

关键词： software module clustering Multi-Objective Optimization Genetic Algorithms

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software Architecture Recovery with Information Fusion 2023

Software Architecture Recovery with Information Fusion

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31st ACM Joint Meeting of the European software Engineering Conference / Symposium on the Foundations-of-software-Engineering (ESEC/FSE)

作者： Zhang, Yiran Xu, Zhengzi Liu, Chengwei Chen, Hongxu Sun, Jianwen Qiu, Dong Liu, Yang Nanyang Technol Univ Singapore Singapore Huawei Technol Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9798400703270

Understanding the architecture is vital for effectively maintaining and managing large software systems. However, as software systems evolve over time, their architectures inevitably change. To keep up with the change, architects need to track the implementation-level changes and update the architectural documentation accordingly, which is time-consuming and error-prone. Therefore, many automatic architecture recovery techniques have been proposed to ease this process. Despite efforts have been made to improve the accuracy of architecture recovery, existing solutions still suffer from two limitations. First, most of them only use one or two type of information for the recovery, ignoring the potential usefulness of other sources. Second, they tend to use the information in a coarse-grained manner, overlooking important details within it. To address these limitations, we propose SARIF, a fully automated architecture recovery technique, which incorporates three types of comprehensive information, including dependencies, code text and folder structure. SARIF can recover architecture more accurately by thoroughly analyzing the details of each type of information and adaptively fusing them based on their relevance and quality. To evaluate SARIF, we collected six projects with published ground-truth architectures and three open-source projects labeled by our industrial collaborators. We compared SARIF with nine state-of-the-art techniques using three commonly-used architecture similarity metrics and two new metrics. The experimental results show that SARIF is 36.1% more accurate than the best of the previous techniques on average. By providing comprehensive architecture, SARIF can help users understand systems effectively and reduce the manual effort of obtaining ground-truth architectures.

关键词： software architecture recovery software module clustering reverse engineering architecture comparison

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Multi-objective general variable neighborhood search for software maintainability optimization

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 133卷

作者： Yuste, Javier Pardo, Eduardo G. Duarte, Abraham Hao, Jin-Kao Univ Rey Juan Carlos C-Tulipan S-N Mostoles Madrid 28933 Spain Univ Angers LERIA 2 Blvd Lavoisier F-49045 Angers France

The quality of software projects is measured by different attributes such as efficiency, security, robustness, or understandability, among others. In this paper, we focus on maintainability by studying the optimization of software modularity, which is one of the most important aspects in this regard. Specifically, we study two well-known and closely related multi-objective optimization problems: the Equal-size Cluster Approach Problem (ECA) and the Maximizing Cluster Approach Problem (MCA). Each of these two problems looks for the optimization of several conflicting and desirable objectives in terms of modularity. To this end, we propose a method based on the Multi-Objective Variable Neighborhood Search (MO-VNS) methodology in combination with a constructive procedure based on Path-Relinking. As far as we know, this is the first time that a method based on MO-VNS is proposed for the MCA and ECA problems. To enhance the performance of the proposed algorithm, we present three advanced strategies: an incremental evaluation of the objective functions, an efficient exploration of promising areas in the search space, and an analysis of the objectives that better serve as guiding functions during the search phase. Our proposal has been validated by experimentally comparing the performance of our algorithm with the best previous state-of-the-art method for the problem and three reference methods for multi-objective optimization. The experiments have been performed on a set of 124 real software instances previously reported in the literature.

关键词： software maintainability Search-based software engineering software module clustering Heuristics Multi-objective optimization

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General Variable Neighborhood Search for the optimization of software quality

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COMPUTERS & OPERATIONS RESEARCH 2024年 165卷

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

In the area of Search -Based software Engineering, software engineering issues are formulated and tackled as optimization problems. Among the problems within this area, the software module clustering Problem (SMCP) consists of finding an organization of a software project that minimizes coupling and maximizes cohesion. Since modular code is easier to understand, the objective of this problem is to increase the quality of software projects, thus increasing their maintainability and reducing the associated costs. In this work we study a recently proposed objective function named Function of Complexity Balance (FCB). Since this problem has been demonstrated to be NP -hard, we propose a new heuristic algorithm based on the General Variable Neighborhood Search (GVNS) schema to tackle the problem. For the GVNS, we propose six different neighborhood structures and categorize them into three different groups. Then, we analyze their contribution to the results obtained by the algorithm. In order to improve the efficiency of the proposed approach, we leverage domain -specific information to perform incremental evaluations of the objective function and to explore only areas of interest in the search space. The proposed algorithm has been tested over a set of real world software repositories, achieving better results than the previous state-of-the-art method, a Hybrid Genetic Algorithm, in terms of both quality and computing times. Furthermore, the relevance of the improvement produced by our proposal has been corroborated by non -parametric statistical tests.

关键词： Variable Neighborhood Search software maintainability Search-Based software Engineering software module clustering Heuristic

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A bioinspired discrete heuristic algorithm to generate the effective structural model of a program source code

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2023年第8期35卷

作者： Arasteh, Bahman Sadegi, Razieh Arasteh, Keyvan Gunes, Peri Kiani, Farzad Torkamanian-Afshar, Mahsa Istinye Univ Fac Engn & Nat Sci Dept Software Engn Istanbul Turkiye Islamic Azad Univ Dept Comp Engn Tabriz Branch Tabriz Iran Yildiz Tech Univ Fac Engn Dept Software Engn Istanbul Turkiye Fatih Sultan Mehmet Vakif Univ Fac Engn Comp Engn Dept TR-34445 Istanbul Turkiye Nisantasi Univ Fac Engn & Architecture Comp Engn Dept TR-34398 Istanbul Turkiye

When the source code of a software is the only product available, program understanding has a substantial influence on software maintenance costs. The main goal in code comprehension is to extract information that is used in the software maintenance stage. Generating the structural model from the source code helps to alleviate the software maintenance cost. software module clustering is thought to be a viable reverse engineering approach for building structural design models from source code. Finding the optimal clustering model is an NP-complete problem. The primary goals of this study are to minimize the number of connections between created clusters, enhance internal connections inside clusters, and enhance clustering quality. The previous approaches' main flaws were their poor success rates, instability, and inadequate modularization quality. The Olympiad optimization algorithm was introduced in this paper as a novel population-based and discrete heuristic algorithm for solving the software module clustering problem. This algorithm was inspired by the competition of a group of students to increase their knowledge and prepare for an Olympiad exam. The suggested algorithm employs a divide-and-conquer strategy, as well as local and global search methodologies. The effectiveness of the suggested Olympiad algorithm to solve the module clustering problem was evaluated using ten real-world and standard software benchmarks. According to the experimental results, on average, the modularization quality of the generated clustered models for the ten benchmarks is about 3.94 with 0.067 standard deviations. The proposed algorithm is superior to the prior algorithms in terms of modularization quality, convergence, and stability of results. Furthermore, the results of the experiments indicate that the proposed algorithm can be used to solve other discrete optimization problems efficiently. (c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. Thi

关键词： Olympiad optimization algorithm software module clustering Cohesion Modularization quality

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An Innovative Approach To software Modularization Based On The Artificial Fish Swarm Algorithm 22

An Innovative Approach To Software Modularization Based On T...

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Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering

作者： Jianqiang Pan Cheng Zhang Huihui Jia Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology State Key Lab for Novel Software Technology Anhui University China

ISBN: (纸本)9781450397148

It gets more and more expensive to maintain the complete software system as time goes on since the software architecture grows more complicated and the software code is more difficult to understand. This issue may be solved by taking the essential parts out of the source code and organizing them into the appropriate subsystems. Hierarchy-based partitioning is more complex and less successful in solving issues with huge software modules. In order to do this, this work suggests an innovative artificial fish swarm method as a meta-heuristic to solve the software module clustering problems (SMCPs) and presents a mutation operator and a local search strategy to handle the premature situation. The performance of the suggested method is assessed on a range of real-world software systems by comparison to the most recent meta-heuristic methods. The outcomes of the experiments demonstrate that the suggested methodology works better than those of other methods.

关键词： software refactoring Artificial fish swarm algorithm software module clustering

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