Paper
DePro: Understanding the Role of LLMs in Debugging Competitive Programming Code
Authors
Nabiha Parvez, Tanvin Sarkar Pallab, Mia Mohammad Imran, Tarannum Shaila Zaman
Abstract
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.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.19399v1</id>\n <title>DePro: Understanding the Role of LLMs in Debugging Competitive Programming Code</title>\n <updated>2026-03-19T18:46:34Z</updated>\n <link href='https://arxiv.org/abs/2603.19399v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.19399v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.SE'/>\n <published>2026-03-19T18:46:34Z</published>\n <arxiv:comment>This paper is accepted in FSE 2026 IVR track!</arxiv:comment>\n <arxiv:primary_category term='cs.SE'/>\n <author>\n <name>Nabiha Parvez</name>\n </author>\n <author>\n <name>Tanvin Sarkar Pallab</name>\n </author>\n <author>\n <name>Mia Mohammad Imran</name>\n </author>\n <author>\n <name>Tarannum Shaila Zaman</name>\n </author>\n </entry>"
}