Comparing Developer and LLM Biases in Code Evaluation
As LLMs are increasingly used as judges in code applications, they should be evaluated in realistic interactive settings that capture partial context and ambiguous intent. We present TRACE (Tool for Rubric Analysis in Code Evaluation), a framework that evaluates LLM judges' ability to predict human preferences and automatically extracts rubric items to reveal systematic biases in how humans and models weigh each item. Across three modalities -- chat-based programming, IDE autocompletion, and instructed code editing -- we use TRACE to measure how well LLM judges align with developer preferences. Among 13 different models, the best judges underperform human annotators by 12-23%. TRACE ident...
| Authors | Aditya Mittal, Ryan Shar, Zichu Wu et al. |
|---|---|
| Primary category | cs.SE |
| Published | 25 March 2026 |
| Last updated at source | 14 May 2026 |
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