Agentic AI Software Engineers: Programming with Trust
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916962634825728 |
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| author | Roychoudhury, Abhik Pasareanu, Corina Pradel, Michael Ray, Baishakhi |
| author_facet | Roychoudhury, Abhik Pasareanu, Corina Pradel, Michael Ray, Baishakhi |
| contents | Large Language Models (LLMs) have shown surprising proficiency in generating code snippets, promising to automate large parts of software engineering via artificial intelligence (AI). We argue that successfully deploying AI software engineers requires a level of trust equal to or even greater than the trust established by human-driven software engineering practices. The recent trend toward LLM agents offers a path toward integrating the power of LLMs to create new code with the power of analysis tools to increase trust in the code. This opinion piece comments on whether LLM agents could dominate software engineering workflows in the future and whether the focus of programming will shift from programming at scale to programming with trust. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_13767 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Agentic AI Software Engineers: Programming with Trust Roychoudhury, Abhik Pasareanu, Corina Pradel, Michael Ray, Baishakhi Software Engineering Artificial Intelligence Large Language Models (LLMs) have shown surprising proficiency in generating code snippets, promising to automate large parts of software engineering via artificial intelligence (AI). We argue that successfully deploying AI software engineers requires a level of trust equal to or even greater than the trust established by human-driven software engineering practices. The recent trend toward LLM agents offers a path toward integrating the power of LLMs to create new code with the power of analysis tools to increase trust in the code. This opinion piece comments on whether LLM agents could dominate software engineering workflows in the future and whether the focus of programming will shift from programming at scale to programming with trust. |
| title | Agentic AI Software Engineers: Programming with Trust |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2502.13767 |