Debugging consumes a substantial portion of the software development lifecycle, yet the effectiveness of Large Language Models(LLMs) in this task is not well understood. Competitive programming offers a rich benchmark for such evaluation, given its diverse problem domains and strict efficiency requirements. We present an empirical study of LLM-based debugging on competitive programming problems and introduce DePro, a test-case driven approach that assists programmers by correcting existing code rather than generating new solutions. DePro combines brute-force reference generation, stress testing, and iterative LLM-guided refinement to identify and resolve errors efficiently.Experiments on 13 faulty user submissions from Codeforces demonstrate that DePro consistently produces correct solutions, reducing debugging attempts by up to 64% and debugging time by an average of 7.6 minutes per problem compared to human programmers and zero-shot LLM debugging.
@article{arxiv.2603.19399,
title = {DePro: Understanding the Role of LLMs in Debugging Competitive Programming Code},
author = {Nabiha Parvez and Tanvin Sarkar Pallab and Mia Mohammad Imran and Tarannum Shaila Zaman},
journal= {arXiv preprint arXiv:2603.19399},
year = {2026}
}