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.
@article{arxiv.2509.25192,
title = {WARP -- Web-Augmented Real-time Program Repairer: A Real-Time Compilation Error Resolution using LLMs and Web-Augmented Synthesis},
author = {Anderson de Lima Luiz},
journal= {arXiv preprint arXiv:2509.25192},
year = {2025}
}