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technologyreview.com·1h ago
AI Research
arXiv CS.AI·Jun 9·5 min read
CodeTaste: Can LLMs Generate Human-Level Code Refactorings?
TL;DR
Software development often leads to complex and poorly structured code, which can hinder maintainability. CodeTaste, a benchmark tool, was developed to evaluate whether large language model (LLM) coding agents can perform effective code refactorings.
✦ Why It Matters
Engineers can leverage insights from CodeTaste to better understand LLM capabilities in code maintenance tasks.
Key Takeaways
- Explore CodeTaste to evaluate LLM refactoring capabilities.
- Consider human developer choices when using LLMs for code generation.
- Monitor LLM outputs for architectural debt in generated code.
- Integrate LLMs cautiously into software maintenance processes.
Full Summary
As software systems evolve, they can accumulate complexity and architectural debt, making them harder to maintain. Refactoring is a process where developers improve code structure without changing its behavior.
CodeTaste was created as a benchmark to assess the capability of LLM coding agents in executing refactorings and identifying those chosen by human developers in actual codebases. The methodology involved analyzing a dataset of real-world code refactorings to compare LLM-generated solutions against human practices.
Results indicated that while LLMs can produce functional code, their refactoring accuracy and alignment with human choices were inconsistent. This highlights the need for further advancements in LLM capabilities to enhance their utility in software maintenance.
The findings suggest that integrating LLMs into development workflows could improve efficiency but require careful oversight.