SVRepair: Structured Visual Reasoning for Automated Program Repair

TL;DR

SVRepair introduces a multimodal framework that leverages structured visual representations to improve automated program repair by transforming visual artifacts into semantic scene graphs for fault localization and patch synthesis.

Contribution

It proposes a novel structured visual representation (SVR) method that converts visual artifacts into semantic scene graphs, enhancing fault localization and repair in multimodal program repair.

Findings

Achieves state-of-the-art accuracy on multiple benchmarks.

Effectively localizes faults using structured visual representations.

Reduces hallucinations through iterative visual-artifact segmentation.

Abstract

Large language models (LLMs) have recently shown strong potential for Automated Program Repair (APR), yet most existing approaches remain unimodal and fail to leverage the rich diagnostic signals contained in visual artifacts such as screenshots and control-flow graphs. In practice, many bug reports convey critical information visually (e.g., layout breakage or missing widgets), but directly using such dense visual inputs often causes context loss and noise, making it difficult for MLLMs to ground visual observations into precise fault localization and executable patches. To bridge this semantic gap, we propose \textbf{SVRepair}, a multimodal APR framework with structured visual representation. SVRepair first fine-tunes a vision-language model, \textbf{Structured Visual Representation (SVR)}, to uniformly transform heterogeneous visual artifacts into a \emph{semantic scene graph} that captures GUI elements and their structural relations (e.g., hierarchy), providing normalized, code-relevant context for downstream repair. Building on the graph, SVRepair drives a coding agent to localize faults and synthesize patches, and further introduces an iterative visual-artifact segmentation strategy that progressively narrows the input to bug-centered regions to suppress irrelevant context and reduce hallucinations. Extensive experiments across multiple benchmarks demonstrate state-of-the-art performance: SVRepair achieves \textbf{36.47\%} accuracy on SWE-Bench M, \textbf{38.02\%} on MMCode, and \textbf{95.12\%} on CodeVision, validating the effectiveness of SVRepair for multimodal program repair.