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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2508.11126 |
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| _version_ | 1866912587082366976 |
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| author | Wang, Huanting Gong, Jingzhi Zhang, Huawei Xu, Jie Wang, Zheng |
| author_facet | Wang, Huanting Gong, Jingzhi Zhang, Huawei Xu, Jie Wang, Zheng |
| contents | AI agentic programming is an emerging paradigm where large language model (LLM)-based coding agents autonomously plan, execute, and interact with tools such as compilers, debuggers, and version control systems. Unlike conventional code generation, these agents decompose goals, coordinate multi-step processes, and adapt based on feedback, reshaping software development practices. This survey provides a timely review of the field, introducing a taxonomy of agent behaviors and system architectures and examining relevant techniques for planning, context management, tool integration, execution monitoring, and benchmarking datasets. We highlight challenges of this fast-moving field and discuss opportunities for building reliable, transparent, and collaborative coding agents. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_11126 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI Agentic Programming: A Survey of Techniques, Challenges, and Opportunities Wang, Huanting Gong, Jingzhi Zhang, Huawei Xu, Jie Wang, Zheng Software Engineering AI agentic programming is an emerging paradigm where large language model (LLM)-based coding agents autonomously plan, execute, and interact with tools such as compilers, debuggers, and version control systems. Unlike conventional code generation, these agents decompose goals, coordinate multi-step processes, and adapt based on feedback, reshaping software development practices. This survey provides a timely review of the field, introducing a taxonomy of agent behaviors and system architectures and examining relevant techniques for planning, context management, tool integration, execution monitoring, and benchmarking datasets. We highlight challenges of this fast-moving field and discuss opportunities for building reliable, transparent, and collaborative coding agents. |
| title | AI Agentic Programming: A Survey of Techniques, Challenges, and Opportunities |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2508.11126 |