AuditCoder: Process-Auditable Code Generation through Responsibility-Preserving Task Graphs

Abstract

Plans can guide code generation without record?ing which code implements each subproblem, leaving reviewers to reconstruct these links dur?ing handoff and maintenance. We present AU?DITCODER, a multi-agent system for process?auditable code generation that jointly con?structs a program and its responsibility record. A contract-annotated task graph assigns sta?ble IDs before implementation and preserves code ownership through generation and assem?bly. The same IDs connect each planning commitment to its code, versioned check results, and repair history, supporting human inspec?tion and automated checks of record complete?ness and repair decisions. When failure evidence supports a node or branch, bounded repair declares an editable scope, records any expansion, and leaves code outside the final scope unchanged. The system abstains when no boundary is supported. On a 200-task APPS sample and ClassEval, AUDITCODER remains competitive with CodeSim across two models under a one-round feedback limit. The DeepSeek-based system also remains compet?itive on 22 exploratory cross-domain tasks. An audit of the DeepSeek APPS run finds that, averaged across tasks, 97.25% of final leaf records retain nonempty code, contract, provenance, and check fields. All 44 repair attempts follow the recorded accept/rollback policy, and 30 pass held-out tests, with scopes averaging 22% of graph nodes. Anonymous repository: https://anonymous.4open.science/r/AuditCoder/.

arXiv commentPreprint. 29 pages, 7 figures. Code and data are available at the project repository