# CI-Repair-Bench: A Repository-Aware Benchmark for Automated Patch Validation via CI Workflows

arXiv: https://arxiv.org/abs/2604.27148 · HTML: https://mlsanity.com/paper/2604.27148/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2026.

## Built on by (most cited first, 2 of 2)

- [Update from Hell: Can Coding Agents Survive Hidden Breakage in Dependency Upgrades?](https://mlsanity.com/paper/2608.30300/) (2026)
- [RETRACE: From Entangled Repair Histories to Reusable Experience for CI Repair](https://mlsanity.com/paper/2610.04658/) (2026)

## Abstract (arXiv)

Continuous Integration (CI) enforces repository-level correctness through multi-stage workflows and is central to modern software development, yet diagnosing and repairing CI failures remains challenging. Unlike traditional program repair, CI failures frequently involve non-code artifacts, environment and dependency issues, noisy execution logs, and workflow-level constraints. Existing program repair benchmarks fall short in this setting: they are largely test-centric, restrict repairs to source code, assume fixed execution environments, and evaluate under simplified CI workflows that do not reflect real repository-level validation. We introduce CI-Repair-Bench, a benchmark for CI-verified, repository-level program repair constructed from real GitHub Actions executions. It contains 567 CI failure instances from 103 repositories and evaluates repair correctness exclusively through full CI re-execution under original workflows. Failures are categorized into 12 CI error types, enabling fine-grained, error-type-aware evaluation. To demonstrate benchmark usage, we include a reference CI repair workflow that analyzes CI logs to localize faults and generate candidate patches. Empirical results show that automated repair is most effective for localized, tool-enforced failures such as formatting and linting, while environment, dependency, and configuration-related failures remain challenging; the best-performing LLM achieves an 18.9% repair success rate. CI-Repair-Bench provides a realistic evaluation foundation for advancing research on CI-native automated program repair.