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 |
| Language: | en |
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Ieee
2025
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| Online Access: | http://eprints.uthm.edu.my/12731/1/J19753_c1c62fb1caac344930dd3101ef4ed248.pdf http://eprints.uthm.edu.my/12731/ https://doi.org/10.1109/ACCESS.2025.3542087 |
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| author | Almogahed, Abdullah Mahdin, Hairulnizam A. Al-Masni, Mohammed Alzaeemi, Shehab Abdulhabib Omar, Mazni Alawadhi, Abdulwadood Barraood, Samera Obaid Gilal, Abdul Rehman Bakather, Adnan Ameen |
| author_facet | Almogahed, Abdullah Mahdin, Hairulnizam A. Al-Masni, Mohammed Alzaeemi, Shehab Abdulhabib Omar, Mazni Alawadhi, Abdulwadood Barraood, Samera Obaid Gilal, Abdul Rehman Bakather, Adnan Ameen |
| author_sort | Almogahed, Abdullah |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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 | my.uthm.eprints-12731 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2025 |
| publisher | Ieee |
| record_format | eprints |
| spelling | my.uthm.eprints-127312025-06-26T00:12:13Z http://eprints.uthm.edu.my/12731/ Multi-Classification Refactoring Framework Using Hopfield Neural Network for Sustainable Software Development Almogahed, Abdullah Mahdin, Hairulnizam A. Al-Masni, Mohammed Alzaeemi, Shehab Abdulhabib Omar, Mazni Alawadhi, Abdulwadood Barraood, Samera Obaid Gilal, Abdul Rehman Bakather, Adnan Ameen QA Mathematics T Technology (General) 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. Ieee 2025 Article PeerReviewed text en http://eprints.uthm.edu.my/12731/1/J19753_c1c62fb1caac344930dd3101ef4ed248.pdf Almogahed, Abdullah and Mahdin, Hairulnizam and A. Al-Masni, Mohammed and Alzaeemi, Shehab Abdulhabib and Omar, Mazni and Alawadhi, Abdulwadood and Barraood, Samera Obaid and Gilal, Abdul Rehman and Bakather, Adnan Ameen (2025) Multi-Classification Refactoring Framework Using Hopfield Neural Network for Sustainable Software Development. Digital Object Identifier, 13. pp. 1-24. https://doi.org/10.1109/ACCESS.2025.3542087 |
| spellingShingle | QA Mathematics T Technology (General) Almogahed, Abdullah Mahdin, Hairulnizam A. Al-Masni, Mohammed Alzaeemi, Shehab Abdulhabib Omar, Mazni Alawadhi, Abdulwadood Barraood, Samera Obaid Gilal, Abdul Rehman Bakather, Adnan Ameen Multi-Classification Refactoring Framework Using Hopfield Neural Network for Sustainable Software Development |
| 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 | QA Mathematics T Technology (General) |
| url | http://eprints.uthm.edu.my/12731/1/J19753_c1c62fb1caac344930dd3101ef4ed248.pdf http://eprints.uthm.edu.my/12731/ https://doi.org/10.1109/ACCESS.2025.3542087 |
| url_provider | http://eprints.uthm.edu.my/ |