CodeHealth Predicts the Token Cost of Agentic Code Review: A Pre-Registered Study of 465 Agent-Authored Pull Requests
Objective: We test whether baseline CodeHealth predicts the token cost of agentic review and fix beyond pull request (PR) footprint.
ProofPaper ↗
Key points
- Context: Organizations increasingly delegate code review to multi-agent large language model (LLM) pipelines billed per token.
- Method: In a pre-registered observational study, we sampled 465 agent-authored PRs from 246 repositories in AIDev, jointly stratified by CodeHealth and footprint under repository caps.
- A frozen five-agent review-and-fix pipeline processed every PR with three different LLMs. Per LLM, we fitted a mixed model of log tokens on CodeHealth and log PR footprint, with a repository random intercept.
- Conclusions: A quality metric calibrated against human maintainability also predicts the inference cost of multi-agent review and fix.
Sources (1)
- [1]CodeHealth Predicts the Token Cost of Agentic Code Review: A Pre-Registered Study of 465 Agent-Authored Pull RequestsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 02:00 PM
Objective: We test whether baseline CodeHealth predicts the token cost of agentic review and fix beyond pull request (PR) footprint.
Context: Organizations increasingly delegate code review to multi-agent large language model (LLM) pipelines billed per token.
Extractive summary: sentences quoted from the sources.
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