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Code Readability in the Age of Large Language Models: An Industrial Case Study from Atlassian

Created by
  • Haebom

Author

Wannita Takerngsaksiri, Chakkrit Tantithamthavorn, Micheal Fu, Jirat Pasuksmit, Kun Chen, Ming Wu

Outline

In this paper, we present the results of a study on how software developers perceive code readability in an era where large-scale language models (LLMs) are used for code generation, and compare the readability of code generated by HULA, an LLM-based software development agent framework, with that of human-written code through real-world scenarios. The survey results confirm that code readability is still an important factor, and show that the readability of code generated by HULA is similar to that of human-written code, which can lead to improved reliability and wider adoption of LLM-based software development platforms.

Takeaways, Limitations

Takeaways:
Even during my LLM years, I have reaffirmed that code readability is a very important factor in software development.
The readability of code generated by the LLM-based code generator (HULA) is similar to human-written code, which can increase the reliability and adoption rate of LLM-based software development platforms.
Demonstrates the practical applicability of LLM-based software development tools such as HULA.
Limitations:
The generalizability of the results may be limited by the number and composition of survey participants.
Since the results are specific to the HULA framework, they may not apply to other LLM-based code generators.
Due to the limited scope of real-world scenarios, it may be difficult to generalize to all software development situations.
Additional review of the objectivity and reliability of the readability evaluation criteria may be necessary.
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