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WARP -- Web-Augmented Real-time Program Repairer: A Real-Time Compilation Error Resolution using LLMs and Web-Augmented Synthesis

Anderson de Lima Luiz

TL;DR

WARP addresses the real-time resolution of compilation errors by uniting a fine-tuned Code-LLM with a Web-Augmented Retrieval module that grounds suggestions in current online knowledge. It ingests error context, generates an internal hypothesis, augments with multi-source web evidence, and synthesizes a final diff-supported fix with explanations and citations. On the ComErrFix-CGP-v1.1 benchmark, WARP achieves a 72.5% CompilesCorrectly fix rate and higher semantic correctness than LLM-only or TFIDF-based baselines, demonstrating robustness to noisy data and API changes. This approach enables interactive, evidence-backed debugging with improved explainability and traceability for developers.

Abstract

Compilation errors represent a significant bottleneck in software development productivity. This paper introduces WARP (Web-Augmented Real-time Program Repairer), a novel system that leverages Large Language Models (LLMs) and dynamic web-augmented synthesis for real-time resolution of these errors. WARP actively monitors developer terminals, intelligently detects compilation errors, and synergistically combines the understanding of a fine-tuned Code-LLM with relevant solutions, explanations, and code snippets retrieved from up-to-date web sources like developer forums and official documentation. Experimental results on our curated benchmark, CGP (featuring C/C++, Python, and Go errors), demonstrate WARP achieves a superior fix rate (72.5 % Compiles correctly) and higher semantic correctness compared to baseline LLM-only approaches and traditional IDE quick-fixes. Key technical challenges in achieving high-accuracy synthesis from noisy web data.

WARP -- Web-Augmented Real-time Program Repairer: A Real-Time Compilation Error Resolution using LLMs and Web-Augmented Synthesis

TL;DR

WARP addresses the real-time resolution of compilation errors by uniting a fine-tuned Code-LLM with a Web-Augmented Retrieval module that grounds suggestions in current online knowledge. It ingests error context, generates an internal hypothesis, augments with multi-source web evidence, and synthesizes a final diff-supported fix with explanations and citations. On the ComErrFix-CGP-v1.1 benchmark, WARP achieves a 72.5% CompilesCorrectly fix rate and higher semantic correctness than LLM-only or TFIDF-based baselines, demonstrating robustness to noisy data and API changes. This approach enables interactive, evidence-backed debugging with improved explainability and traceability for developers.

Abstract

Compilation errors represent a significant bottleneck in software development productivity. This paper introduces WARP (Web-Augmented Real-time Program Repairer), a novel system that leverages Large Language Models (LLMs) and dynamic web-augmented synthesis for real-time resolution of these errors. WARP actively monitors developer terminals, intelligently detects compilation errors, and synergistically combines the understanding of a fine-tuned Code-LLM with relevant solutions, explanations, and code snippets retrieved from up-to-date web sources like developer forums and official documentation. Experimental results on our curated benchmark, CGP (featuring C/C++, Python, and Go errors), demonstrate WARP achieves a superior fix rate (72.5 % Compiles correctly) and higher semantic correctness compared to baseline LLM-only approaches and traditional IDE quick-fixes. Key technical challenges in achieving high-accuracy synthesis from noisy web data.

Paper Structure

This paper contains 13 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. The WARP System: Architecture and Methodology
  4. Real-time Error Ingestion and Contextual Feature Extraction
  5. LLM-Internal Hypothesis Generation
  6. Web-Augmented Retrieval (WAR) Module
  7. LLM-based Synthesis and Solution Refinement
  8. Experimental Evaluation
  9. Baselines and Metrics
  10. Results and Discussion
  11. Ablation Study: Impact of WAR Module and Synthesis
  12. Discussion, Challenges, and Future Work
  13. Conclusion

Figures (2)

  • Figure 1: Interface of the WARP system, displaying a detected error, the LLM-generated explanation, suggested fix (diff), and supporting web evidence. This interface aims for clarity and actionable insights for the developer.
  • Figure 2: High-level architecture of the WARP system, illustrating information flow from real-time error capture, through contextual feature extraction, initial LLM analysis, web augmentation via the WAR-Module, and final LLM-driven synthesis, before presenting the solution to the developer.