# DePro: Understanding the Role of LLMs in Debugging Competitive Programming Code

arXiv: https://arxiv.org/abs/2603.19399 · HTML: https://mlsanity.com/paper/2603.19399/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2026.

## Built on by (most cited first, 2 of 2)

- [A-ProS: Towards Reliable Autonomous Programming Through Multi-Model Feedback](https://mlsanity.com/paper/2605.18073/) (2026)
- [VibeCheck: Assessing the Quality of LLM-Generated Unit Tests: A Multi-agent Empirical Study across Heterogeneous Repositories](https://mlsanity.com/paper/2609.05978/) (2026)

## Abstract (arXiv)

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.