Multimodal Context Enhances DeepSeek-R1 Code Repair Performance on FeedbackEval

Abstract

Abstract: This report synthesises findings from 11 peer-reviewed papers addressing the following research question: How does multimodal context (text + code diagrams) affect the iterative code repair performance of DeepSeek-R1 on FeedbackEval compared to text-only context, measured by repair success rate and token. Code repair is a fundamental task in software development, facilitating efficient bug resolution and software maintenance. Although large language models (LLMs) have demonstrated considerable potential in automated code repair, their ability to comprehend and leverage diverse. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 3.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does multimodal context (text + code diagrams) affect the iterative code repair performance of DeepSeek-R1 on FeedbackEval compared to text-only context, measured by repair success rate and token efficiency?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance