ReCAP: Learning Problem-Level Repair Experience from Entangled Pull-Request Histories

Large language model (LLM) agents increasingly reuse experience from prior tasks, but existing methods generally assume that each experience is already aligned with a known problem and solution. Real software development histories lack this alignment: a completed pull request (PR) may contain multiple continuous integration (CI) problems, intermediate repair attempts, reverted edits, and unrelated changes. The final passing revision shows that the CI workflow passes after all changes are applied, but not which changes resolved each problem. We introduce ReCAP, which learns problem-level repair experience from such histories. ReCAP attributes developer changes to CI problems through two views: the endpoint view reasons backward from changes retained in the passing revision, while the development view traces forward through commit history to capture how repairs evolved. It reconciles the two views using CI execution evidence and represents each repair at three abstraction levels, from concrete changes to transferable repair patterns. For a new CI failure, ReCAP retrieves and filters problem-specific experience to guide repair. We evaluate ReCAP on CI-REPAIR-BENCH, containing 565 PR-level repairs from 101 repositories across 12 failure categories. With mini-SWE-agent, ReCAP improves Pass@1 from 19.6% to 31.9% using MiniMax-M2.5 and from 23.3% to 32.8% using DeepSeek-V4-Flash; with Codex, it improves Pass@1 from 15.5% to 27.5% on a matched subset. Combining endpoint and development evidence consistently outperforms either view alone, showing that recovering problem–change alignment allows noisy development histories to be reused for CI repair.