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6 results on '"software modularisation"'

1. How Starting Points and Representations Affect Software Modularisation: An Empirical Analysis

Author

Maramazi, Faisal, Odebode, Afees, Mann, Ashley, Swift, Stephen, Arzoky, Mahir, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Arai, Kohei, editor

Published
2024
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3. Using multi‐pattern clustering methods to improve software maintenance quality

Author

Yi‐Ting Chen, Chin‐Yu Huang, and Tsung‐Han Yang

Subjects
cluster analysis, maintainability, software architecture recovery, software clustering, software modularisation, Computer software, QA76.75-76.765
Abstract

Abstract In software engineering, a software development process, also known as software development life cycle (SDLC), involves several distinct activities for developing, testing, maintaining, and evolving a software system. Within the stages of SDLC, software maintenance occupies most of the total cost of the software life. However, after extended maintenance activities, software quality always degrades due to increasing size and complexity. To solve this problem, software modularisation using clustering is an intuitive way to modularise and classify code into small pieces. , A multi‐pattern clustering (MPC) algorithm for software modularisation is proposed in this study. The proposed MPC algorithm can be divided into five different steps: (1) preprocessing, (2) file labelling, (3) collection of chain dependencies, (4) hierarchical agglomerative clustering, (5) modification of the clustering result. The performance of the proposed MPC algorithm to selected clustering techniques is compared by using three open‐source and one closed‐source software programs. Experimental results show that the modularisation quality of the proposed MPC algorithm is nearly 1.6 times better than that of the expert decomposition. Additionally, compared to other software clustering algorithms, the proposed MPC algorithm, on average, has a 13% enhancement in producing results similar to human thinking. Consequently, it can be seen that the proposed MPC algorithm is suitable for human comprehension while producing better module quality compared to other clustering algorithms.

Published
2023
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4. Using multi‐pattern clustering methods to improve software maintenance quality.

Author

Chen, Yi‐Ting, Huang, Chin‐Yu, and Yang, Tsung‐Han

Subjects
COMPUTER software quality control, SOFTWARE maintenance, SOFTWARE engineering, COMPUTER software development, SYSTEMS software, HIERARCHICAL clustering (Cluster analysis)
Abstract

In software engineering, a software development process, also known as software development life cycle (SDLC), involves several distinct activities for developing, testing, maintaining, and evolving a software system. Within the stages of SDLC, software maintenance occupies most of the total cost of the software life. However, after extended maintenance activities, software quality always degrades due to increasing size and complexity. To solve this problem, software modularisation using clustering is an intuitive way to modularise and classify code into small pieces. , A multi‐pattern clustering (MPC) algorithm for software modularisation is proposed in this study. The proposed MPC algorithm can be divided into five different steps: (1) preprocessing, (2) file labelling, (3) collection of chain dependencies, (4) hierarchical agglomerative clustering, (5) modification of the clustering result. The performance of the proposed MPC algorithm to selected clustering techniques is compared by using three open‐source and one closed‐source software programs. Experimental results show that the modularisation quality of the proposed MPC algorithm is nearly 1.6 times better than that of the expert decomposition. Additionally, compared to other software clustering algorithms, the proposed MPC algorithm, on average, has a 13% enhancement in producing results similar to human thinking. Consequently, it can be seen that the proposed MPC algorithm is suitable for human comprehension while producing better module quality compared to other clustering algorithms. [ABSTRACT FROM AUTHOR]

Published
2023
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5. Enterprise-level search-based software remodularisation using domain knowledge

Author

de Vries, Rolf (author) and de Vries, Rolf (author)

Abstract

As software systems evolve over time, the quality of its structure and code degrade unless developers regularly maintain it, requiring significant effort. Automated tools to help developers maintain software have been well-studied in the past. In particular, software remodularisation tools focus on improving the code structure quality with minimal effort by suggesting changes to the developers to obtain an improved modularisation. While there has been considerable research on automated software remodularisation, it often faces one or more of the following three shortcomings. First, the approach is applied to small or medium-size codebases, raising the question of whether it scales to large codebases. Second, the results are not validated by the developers of these codebases. Last, the algorithm optimises only from a code quality metrics point of view, not considering the perspective and knowledge of developers. In this thesis, we propose an approach to capture developers' domain knowledge of a large-scale object-oriented codebase, which uses an NSGA-III algorithm to suggest remodularisations that improve code structure quality and adhere to developer knowledge. Additionally, the results of the algorithm are validated by the developers. The results in this thesis show that with little effort, the domain knowledge of developers can be captured and used to improve the suggestions made by the algorithm., Computer Science

Published
2022

6. Clustering-based software modularisation models for resource management in enterprise systems.

Author

Li, Jiahua and Yamini, Ali

Subjects
RESOURCE management, SYSTEMS software, COMPUTER software, MODULAR design, BUSINESS enterprises, ENTERPRISE resource planning software
Abstract

Software module clustering approaches can provide a better understanding of large and complex software systems through decomposing the enterprise resources into classified modules which are smaller, and therefore easier-to-handle. As the dimensions and complexity of enterprise software projects are continuously increasing, handling a large software project is going to be more challenging. The challenge would be more complex if the experienced personnel is considered as well. Therefore, appropriate automatic software modularization clustering methods are required in resource management. This paper provides a Systematic Literature Review (SLR) on the software modularization clustering models. We studied a wide range of papers from 2001 to 2020 to provide our SLR. Also, a technical taxonomy is presented to classify the existing papers on software modularization clustering models and algorithms. The software module clustering methods are categorized into three main classes. Finally, new challenges and forthcoming issues of software modularization clustering models are presented. [ABSTRACT FROM AUTHOR]

Published
2022
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