ROSE: Transformer-Based Refactoring Recommendation for Architectural Smells
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918098227953664 |
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| author | Nursapa, Samal Samuilova, Anastassiya Bucaioni, Alessio Nguyen, Phuong T. |
| author_facet | Nursapa, Samal Samuilova, Anastassiya Bucaioni, Alessio Nguyen, Phuong T. |
| contents | Architectural smells such as God Class, Cyclic Dependency, and Hub-like Dependency degrade software quality and maintainability. Existing tools detect such smells but rarely suggest how to fix them. This paper explores the use of pre-trained transformer models--CodeBERT and CodeT5--for recommending suitable refactorings based on detected smells. We frame the task as a three-class classification problem and fine-tune both models on over 2 million refactoring instances mined from 11,149 open-source Java projects. CodeT5 achieves 96.9% accuracy and 95.2% F1, outperforming CodeBERT and traditional baselines. Our results show that transformer-based models can effectively bridge the gap between smell detection and actionable repair, laying the foundation for future refactoring recommendation systems. We release all code, models, and data under an open license to support reproducibility and further research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_12561 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ROSE: Transformer-Based Refactoring Recommendation for Architectural Smells Nursapa, Samal Samuilova, Anastassiya Bucaioni, Alessio Nguyen, Phuong T. Software Engineering Architectural smells such as God Class, Cyclic Dependency, and Hub-like Dependency degrade software quality and maintainability. Existing tools detect such smells but rarely suggest how to fix them. This paper explores the use of pre-trained transformer models--CodeBERT and CodeT5--for recommending suitable refactorings based on detected smells. We frame the task as a three-class classification problem and fine-tune both models on over 2 million refactoring instances mined from 11,149 open-source Java projects. CodeT5 achieves 96.9% accuracy and 95.2% F1, outperforming CodeBERT and traditional baselines. Our results show that transformer-based models can effectively bridge the gap between smell detection and actionable repair, laying the foundation for future refactoring recommendation systems. We release all code, models, and data under an open license to support reproducibility and further research. |
| title | ROSE: Transformer-Based Refactoring Recommendation for Architectural Smells |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2507.12561 |