Abstract
Debugging consumes a substantial portion of the software development lifecycle, yet researchers do not yet understand well the effectiveness of Large Language Models (LLMs) in this task. 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 efficiently identify and resolve errors. 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.
Recommended Citation
N. Parvez et al., "DePro: Understanding The Role Of LLMs In Debugging Competitive Programming Code," Fse Companion 2026 Proceedings of the 34th ACM International Conference on the Foundations of Software Engineering, pp. 1227 - 1231, Association for Computing Machinery, Jul 2026.
The definitive version is available at https://doi.org/10.1145/3803437.3805561
Department(s)
Computer Science
Publication Status
Open Access
Keywords and Phrases
competitive programming; debugging; empirical study; large language models; manual study
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2026 The Author(s), All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
17 Jul 2026
Comments
National Science Foundation, Grant CCF-2348277