Multi-Classification Refactoring Framework Using Hopfield Neural Network for Sustainable Software Development
Adopting high-quality source code is the ultimate way through which software evolution can be ensured as sustainable. Continuous refactoring in complex software systems ensures longevity and increases architecture knowledge sustainability. However, decision-making about refactoring is a challenge be...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10887181/ |
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| author | Abdullah Almogahed Hairulnizam Mahdin Yeong Hyeon Gu Mohammed A. Al-Masni Shehab Abdulhabib Alzaeemi Mazni Omar Abdulwadood Alawadhi Samera Obaid Barraood Abdul Rehman Gilal Adnan Ameen Bakather |
| author_facet | Abdullah Almogahed Hairulnizam Mahdin Yeong Hyeon Gu Mohammed A. Al-Masni Shehab Abdulhabib Alzaeemi Mazni Omar Abdulwadood Alawadhi Samera Obaid Barraood Abdul Rehman Gilal Adnan Ameen Bakather |
| author_sort | Abdullah Almogahed |
| collection | DOAJ |
| description | Adopting high-quality source code is the ultimate way through which software evolution can be ensured as sustainable. Continuous refactoring in complex software systems ensures longevity and increases architecture knowledge sustainability. However, decision-making about refactoring is a challenge because the benefits of refactoring are vague and very difficult for the developers to quantify, as different refactoring strategies have different effects on quality attributes. No research has developed a multi-classification refactoring framework using artificial neural networks (ANN), specifically hopfield neural networks (HNN), to classify refactoring strategies and improve external software quality and sustainability. Therefore, this study proposes a multi-classification refactoring framework using HNN that classifies refactoring strategies by their impact on external quality attributes. Five stages have been conducted to perform this study, including selecting case studies, identifying the external quality attributes, identifying the most commonly used refactoring strategies in practice, conducting the experiments, and conducting the classification process using HNN. The proposed framework categorizes the refactoring strategies into three categories (positive, negative, and ineffective). By providing clear classifications and descriptions of each strategy, the proposed framework helps developers make informed decisions about how to improve the design and structure of their code. It helps developers mitigate risks associated with code changes by providing guidance on which strategies are likely to yield positive results for specific quality attributes. The proposed multi-classification refactoring framework enhances software sustainability by enhancing critical quality attributes. It supports maintainability, adaptability, and long-term viability, helping to ensure that the software systems remain relevant, efficient, and valuable over time. |
| format | Article |
| id | doaj-art-9fd4e39b6cfe40b1aa0bc4b25f2d17ec |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9fd4e39b6cfe40b1aa0bc4b25f2d17ec2025-08-20T03:00:29ZengIEEEIEEE Access2169-35362025-01-0113317853180810.1109/ACCESS.2025.354208710887181Multi-Classification Refactoring Framework Using Hopfield Neural Network for Sustainable Software DevelopmentAbdullah Almogahed0https://orcid.org/0000-0001-5408-1529Hairulnizam Mahdin1https://orcid.org/0000-0002-2275-0094Yeong Hyeon Gu2https://orcid.org/0000-0002-0002-9386Mohammed A. Al-Masni3https://orcid.org/0000-0002-1548-965XShehab Abdulhabib Alzaeemi4https://orcid.org/0000-0002-6884-0714Mazni Omar5https://orcid.org/0000-0003-1816-2940Abdulwadood Alawadhi6https://orcid.org/0000-0003-3699-0002Samera Obaid Barraood7Abdul Rehman Gilal8https://orcid.org/0000-0002-1904-1588Adnan Ameen Bakather9Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, MalaysiaFaculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, MalaysiaDepartment of Artificial Intelligence and Data Science, College of AI Convergence, Sejong University, Seoul, Republic of KoreaDepartment of Artificial Intelligence and Data Science, College of AI Convergence, Sejong University, Seoul, Republic of KoreaInterdisciplinary Research Center in Finance and Digital Economy, Business School, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaSchool of Computing, Universiti Utara Malaysia, Sintok, MalaysiaSchool of Computing, Universiti Utara Malaysia, Sintok, MalaysiaDepartment of Computer Science, College of Computers and Information Technology, Hadhramout University, Hadhramaut, YemenKnight Foundation School of Computing and Information Sciences, Florida International University, Miami, FL, USAInterdisciplinary Research Center in Finance and Digital Economy, Business School, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaAdopting high-quality source code is the ultimate way through which software evolution can be ensured as sustainable. Continuous refactoring in complex software systems ensures longevity and increases architecture knowledge sustainability. However, decision-making about refactoring is a challenge because the benefits of refactoring are vague and very difficult for the developers to quantify, as different refactoring strategies have different effects on quality attributes. No research has developed a multi-classification refactoring framework using artificial neural networks (ANN), specifically hopfield neural networks (HNN), to classify refactoring strategies and improve external software quality and sustainability. Therefore, this study proposes a multi-classification refactoring framework using HNN that classifies refactoring strategies by their impact on external quality attributes. Five stages have been conducted to perform this study, including selecting case studies, identifying the external quality attributes, identifying the most commonly used refactoring strategies in practice, conducting the experiments, and conducting the classification process using HNN. The proposed framework categorizes the refactoring strategies into three categories (positive, negative, and ineffective). By providing clear classifications and descriptions of each strategy, the proposed framework helps developers make informed decisions about how to improve the design and structure of their code. It helps developers mitigate risks associated with code changes by providing guidance on which strategies are likely to yield positive results for specific quality attributes. The proposed multi-classification refactoring framework enhances software sustainability by enhancing critical quality attributes. It supports maintainability, adaptability, and long-term viability, helping to ensure that the software systems remain relevant, efficient, and valuable over time.https://ieeexplore.ieee.org/document/10887181/Refactoringrefactoring strategiessoftware qualitysustainabilitymulti-classificationmachine learning |
| spellingShingle | Abdullah Almogahed Hairulnizam Mahdin Yeong Hyeon Gu Mohammed A. Al-Masni Shehab Abdulhabib Alzaeemi Mazni Omar Abdulwadood Alawadhi Samera Obaid Barraood Abdul Rehman Gilal Adnan Ameen Bakather Multi-Classification Refactoring Framework Using Hopfield Neural Network for Sustainable Software Development IEEE Access Refactoring refactoring strategies software quality sustainability multi-classification machine learning |
| title | Multi-Classification Refactoring Framework Using Hopfield Neural Network for Sustainable Software Development |
| title_full | Multi-Classification Refactoring Framework Using Hopfield Neural Network for Sustainable Software Development |
| title_fullStr | Multi-Classification Refactoring Framework Using Hopfield Neural Network for Sustainable Software Development |
| title_full_unstemmed | Multi-Classification Refactoring Framework Using Hopfield Neural Network for Sustainable Software Development |
| title_short | Multi-Classification Refactoring Framework Using Hopfield Neural Network for Sustainable Software Development |
| title_sort | multi classification refactoring framework using hopfield neural network for sustainable software development |
| topic | Refactoring refactoring strategies software quality sustainability multi-classification machine learning |
| url | https://ieeexplore.ieee.org/document/10887181/ |
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