Automatic Programming: Large Language Models and Beyond
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916247800643584 |
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| author | Lyu, Michael R. Ray, Baishakhi Roychoudhury, Abhik Tan, Shin Hwei Thongtanunam, Patanamon |
| author_facet | Lyu, Michael R. Ray, Baishakhi Roychoudhury, Abhik Tan, Shin Hwei Thongtanunam, Patanamon |
| contents | Automatic programming has seen increasing popularity due to the emergence of tools like GitHub Copilot which rely on Large Language Models (LLMs). At the same time, automatically generated code faces challenges during deployment due to concerns around quality and trust. In this article, we study automated coding in a general sense and study the concerns around code quality, security and related issues of programmer responsibility. These are key issues for organizations while deciding on the usage of automatically generated code. We discuss how advances in software engineering such as program repair and analysis can enable automatic programming. We conclude with a forward looking view, focusing on the programming environment of the near future, where programmers may need to switch to different roles to fully utilize the power of automatic programming. Automated repair of automatically generated programs from LLMs, can help produce higher assurance code from LLMs, along with evidence of assurance |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_02213 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automatic Programming: Large Language Models and Beyond Lyu, Michael R. Ray, Baishakhi Roychoudhury, Abhik Tan, Shin Hwei Thongtanunam, Patanamon Software Engineering Artificial Intelligence Machine Learning Automatic programming has seen increasing popularity due to the emergence of tools like GitHub Copilot which rely on Large Language Models (LLMs). At the same time, automatically generated code faces challenges during deployment due to concerns around quality and trust. In this article, we study automated coding in a general sense and study the concerns around code quality, security and related issues of programmer responsibility. These are key issues for organizations while deciding on the usage of automatically generated code. We discuss how advances in software engineering such as program repair and analysis can enable automatic programming. We conclude with a forward looking view, focusing on the programming environment of the near future, where programmers may need to switch to different roles to fully utilize the power of automatic programming. Automated repair of automatically generated programs from LLMs, can help produce higher assurance code from LLMs, along with evidence of assurance |
| title | Automatic Programming: Large Language Models and Beyond |
| topic | Software Engineering Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2405.02213 |