Enhancing Interpretability in Software Change Management with Chain-of-Thought Reasoning
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866912478825283584 |
|---|---|
| author | Sun, Yongqian Kuang, Weihua Shen, Chao Wen, Xidao Zheng, Tinghua Liu, Heng Zhang, Shenglin Wu, Bo Pei, Dan |
| author_facet | Sun, Yongqian Kuang, Weihua Shen, Chao Wen, Xidao Zheng, Tinghua Liu, Heng Zhang, Shenglin Wu, Bo Pei, Dan |
| contents | In modern online services, frequent software changes introduce significant risks. To tackle this challenge, we propose SCELM (Software Change Evaluation and Lifecycle Management), an end-to-end automated framework for software change management. SCELM aims to manage software changes efficiently and precisely, significantly reducing service failures and economic losses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_09315 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Interpretability in Software Change Management with Chain-of-Thought Reasoning Sun, Yongqian Kuang, Weihua Shen, Chao Wen, Xidao Zheng, Tinghua Liu, Heng Zhang, Shenglin Wu, Bo Pei, Dan Software Engineering Artificial Intelligence In modern online services, frequent software changes introduce significant risks. To tackle this challenge, we propose SCELM (Software Change Evaluation and Lifecycle Management), an end-to-end automated framework for software change management. SCELM aims to manage software changes efficiently and precisely, significantly reducing service failures and economic losses. |
| title | Enhancing Interpretability in Software Change Management with Chain-of-Thought Reasoning |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2507.09315 |