Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866909667104391168 |
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| author | Rao, Hongzhou Zhao, Yanjie Hou, Xinyi Wang, Shenao Wang, Haoyu |
| author_facet | Rao, Hongzhou Zhao, Yanjie Hou, Xinyi Wang, Shenao Wang, Haoyu |
| contents | The rapid advancement of large language models (LLMs) has redefined artificial intelligence (AI), pushing the boundaries of AI research and enabling unbounded possibilities for both academia and the industry. However, LLM development faces increasingly complex challenges throughout its lifecycle, yet no existing research systematically explores these challenges and solutions from the perspective of software engineering (SE) approaches. To fill the gap, we systematically analyze research status throughout the LLM development lifecycle, divided into six phases: requirements engineering, dataset construction, model development and enhancement, testing and evaluation, deployment and operations, and maintenance and evolution. We then conclude by identifying the key challenges for each phase and presenting potential research directions to address these challenges. In general, we provide valuable insights from an SE perspective to facilitate future advances in LLM development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_23762 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead Rao, Hongzhou Zhao, Yanjie Hou, Xinyi Wang, Shenao Wang, Haoyu Software Engineering Artificial Intelligence The rapid advancement of large language models (LLMs) has redefined artificial intelligence (AI), pushing the boundaries of AI research and enabling unbounded possibilities for both academia and the industry. However, LLM development faces increasingly complex challenges throughout its lifecycle, yet no existing research systematically explores these challenges and solutions from the perspective of software engineering (SE) approaches. To fill the gap, we systematically analyze research status throughout the LLM development lifecycle, divided into six phases: requirements engineering, dataset construction, model development and enhancement, testing and evaluation, deployment and operations, and maintenance and evolution. We then conclude by identifying the key challenges for each phase and presenting potential research directions to address these challenges. In general, we provide valuable insights from an SE perspective to facilitate future advances in LLM development. |
| title | Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2506.23762 |