Bias in the Loop: Auditing LLM-as-a-Judge for Software Engineering
Large Language Models are increasingly used as judges to evaluate code artifacts when exhaustive human review or executable test coverage is unavailable. LLM-judge is increasingly relevant in agentic software engineering workflows, where it can help rank candidate solutions and guide patch selection. While attractive for scale, current practice lacks a principled account of reliability and bias: repeated evaluations of the same case can disagree; small prompt edits can swing outcomes; and seemingly semantics-preserving, human-equivalent perturbations may elicit divergent verdicts. This paper studies LLM-as-a-Judge for code through a measurement-first lens. We analyze two pointwise judging...
| Authors | Zixiao Zhao, Amirreza Esmaeili, Fatemeh Fard |
|---|---|
| Primary category | cs.SE |
| Published | 18 April 2026 |
| Last updated at source | 18 April 2026 |
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