TraceGate: A Framework for Policy-Controlled Failure Evidence in LLM-Assisted Program Repair

TraceGate is a reusable research artifact for policy-controlled disclosure of crash evidence in LLM-assisted program repair. Rather than relying on fixed evidence bundles or unconstrained LLM-driven tool usage, TraceGate mediates the repair process by capturing structured crash snapshots, selecting evidence and budget actions according to configurable policies, materializing the selected evidence for existing repair backends, validating candidate patches, and logging decisions and outcomes. The artifact provides backend adapters, benchmark workflows, evaluation scripts, and recorded logs to enable reproducible studies of failure-time observability and cost-success trade-offs. In evaluations across four Python benchmarks, five repair backends, and three model sizes, TraceGate recovered an average of 20.5% of residual failures, i.e., tasks that remained unsolved after the underlying backends had exhausted their own repair attempts.
Screencast: https://youtu.be/Gcr6A7tVBis Artifact: https://github.com/NicolasSchuler/tracegate Data: https://zenodo.org/records/19678467