Predicting Acceptance and Review Effort in Human and Agent Pull Requests

Jul 15, 2026

A new preprint on arXiv explores whether the acceptance and review effort of pull requests (PRs) can be predicted at the time of submission, before any reviewer discussion or CI feedback. Using the AIDev dataset, the authors show that tree-based machine learning models can predict PR acceptance with high accuracy (F1 scores above 0.95), but predicting review effort is much more challenging. The study finds that early PR models can help with triage, though review effort remains difficult to estimate from submission-time features alone.

Why it matters: This work provides early evidence that machine learning can assist maintainers in triaging both human- and AI-generated PRs, but highlights the limitations of automating review effort predictions.

Full story at: arXiv Software Engineering ↗