Code Readability in the Age of Large Language Models: An Industrial Case Study from Atlassian
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arXiv
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| Format: | Preprint |
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2025
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| author | Takerngsaksiri, Wannita Tantithamthavorn, Chakkrit Fu, Micheal Pasuksmit, Jirat Chen, Kun Wu, Ming |
| author_facet | Takerngsaksiri, Wannita Tantithamthavorn, Chakkrit Fu, Micheal Pasuksmit, Jirat Chen, Kun Wu, Ming |
| contents | Software engineers spend a significant amount of time reading code during the software development process, especially in the age of large language models (LLMs) that can automatically generate code. However, little is known about the readability of the LLM-generated code and whether it is still important from practitioners' perspectives in this new era. In this paper, we conduct a survey to explore the practitioners' perspectives on code readability in the age of LLMs and investigate the readability of our LLM-based software development agents framework, HULA, by comparing its generated code with human-written code in real-world scenarios. Overall, the findings underscore that (1) readability remains a critical aspect of software development; (2) the readability of our LLM-generated code is comparable to human-written code, fostering the establishment of appropriate trust and driving the broad adoption of our LLM-powered software development platform. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_11264 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Code Readability in the Age of Large Language Models: An Industrial Case Study from Atlassian Takerngsaksiri, Wannita Tantithamthavorn, Chakkrit Fu, Micheal Pasuksmit, Jirat Chen, Kun Wu, Ming Software Engineering Artificial Intelligence Computation and Language Software engineers spend a significant amount of time reading code during the software development process, especially in the age of large language models (LLMs) that can automatically generate code. However, little is known about the readability of the LLM-generated code and whether it is still important from practitioners' perspectives in this new era. In this paper, we conduct a survey to explore the practitioners' perspectives on code readability in the age of LLMs and investigate the readability of our LLM-based software development agents framework, HULA, by comparing its generated code with human-written code in real-world scenarios. Overall, the findings underscore that (1) readability remains a critical aspect of software development; (2) the readability of our LLM-generated code is comparable to human-written code, fostering the establishment of appropriate trust and driving the broad adoption of our LLM-powered software development platform. |
| title | Code Readability in the Age of Large Language Models: An Industrial Case Study from Atlassian |
| topic | Software Engineering Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2501.11264 |