DPO-F+: Aligning Code Repair Feedback with Developers' Preferences

Abstract

Large Language Models (LLMs) are increasingly used in software engineering tasks, especially code repair. However, developers often struggle to interpret model outputs, limiting effective human--AI teaming, where humans and AI work toward a shared objective. Prior work mainly optimizes generated code, giving less attention to natural-language feedback that supports comprehension and iterative improvement. We present \textsc{DPO-f+}, a framework that aligns code-repair feedback with the needs of different developer groups, including novices and proficient developers. It (1) defines feedback-alignment metrics across seven fixed dimensions with task-specific descriptions; (2) automatically constructs pairwise preference datasets from code-repair tasks; (3) fine-tunes models using Direct Preference Optimization (DPO) augmented with a reward model; and (4) provides an automated protocol for evaluating feedback quality. Empirically, \textsc{DPO-f+} outperforms both the baseline and standard DPO in feedback accuracy and overall alignment. On novice programming tasks, \textsc{DPO-f+} improves Pass@1 by 5.71 percentage points (pp) over the baseline and 3.30 pp over DPO. On \textsc{SWE-Bench}, it improves issue-resolution rate by 1.67 pp over DPO and 4.67 pp over the baseline. It also improves feedback alignment by both LLM judges and a human study with 200 developers: beginners preferred \textsc{DPO-f+} in 71.5% of comparisons, with overall preference above chance ($p=0.0057$). By better aligning feedback with developer needs, \textsc{DPO-f+} turns LLM assistance from a one-shot output into a collaborative sense-making workflow, enhancing code comprehension and human--AI teaming in software engineering.