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May 2, 2026Open Access

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

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Authors

NPNabiha ParvezTPTanvin Sarkar PallabMIMia Mohammad Imran

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Overview

Randomized trial evaluates debugging efficiency of LLM-assisted code corrections in competitive programmers, indicating enhanced performance.

Key Points

  • This research aims to evaluate the effectiveness of Large Language Models in debugging competitive programming code.
  • Empirical study using a test-case driven approach with 13 faulty user submissions from Codeforces.
  • Implemented DePro, a system combining brute-force reference generation, stress testing, and iterative LLM-guided refinement to correct code.
  • Compared performance against human programmers and zero-shot LLM debugging for effectiveness.
  • DePro produced correct solutions in 100% of cases tested.
  • Reduced debugging attempts by up to 64% compared to human programming efforts.
  • DePro decreased average debugging time by 7.6 minutes per problem relative to other methods.

Cite This Study

Parvez et al. (2026) studied this question.

synapsesocial.com/papers/69f5943c71405d493affefd2https://doi.org/10.13016/m2db25-prl0
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