Automated Program Repair And Reinforcement Learning For Software Reasoning beyond Nominal Performance: Mechanisms, Uncertainty, and Deployment

Abstract

The literature on automated program repair and reinforcement learning for software reasoning contains a recurring tension between methodological novelty and evidential comparability. By reading execution-grounded reinforcement learning with sequence- and line-level reward models alongside multi-agent chain-of-draft reasoning optimized with reinforcement learning, this article clarifies the conditions under which their conclusions can support a common research argument. A structured reading of two target studies and 12 verified companion references is conducted across five lenses: execution signals, credit assignment, patch validity, cross-language transfer, benchmark design. Emphasis is placed on the provenance of evidence, the comparability of baselines, and the consequences of alternative explanations. The synthesis shows that execution signals cannot be interpreted independently of credit assignment, while patch validity determines whether an apparent improvement remains meaningful outside the original setting. The strongest claims are therefore those that expose sensitivity, failure conditions, and residual uncertainty. The contribution is a decision-oriented synthesis that connects method selection to failure cost and treats reproducibility, provenance, and bounded generalization as first-order design requirements.