CodeContests+: High-Quality Test Case Generation for Competitive Programming

• URL: http://arxiv.org/abs/2506.05817v1
• Date: Fri, 06 Jun 2025 07:29:01 GMT
• Title: CodeContests+: High-Quality Test Case Generation for Competitive Programming
• Authors: Zihan Wang, Siyao Liu, Yang Sun, Hongyan Li, Kai Shen,
• Abstract summary: We introduce an agent system that creates high-quality test cases for competitive programming problems.<n>We apply this system to the CodeContests dataset and propose a new version with improved test cases, named CodeContests+.<n>The results indicate that CodeContests+ achieves significantly higher accuracy than CodeContests, particularly with a notably higher True Positive Rate (TPR)
• Score: 14.602111331209203
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Competitive programming, due to its high reasoning difficulty and precise correctness feedback, has become a key task for both training and evaluating the reasoning capabilities of large language models (LLMs). However, while a large amount of public problem data, such as problem statements and solutions, is available, the test cases of these problems are often difficult to obtain. Therefore, test case generation is a necessary task for building large-scale datasets, and the quality of the test cases directly determines the accuracy of the evaluation. In this paper, we introduce an LLM-based agent system that creates high-quality test cases for competitive programming problems. We apply this system to the CodeContests dataset and propose a new version with improved test cases, named CodeContests+. We evaluated the quality of test cases in CodeContestsPlus. First, we used 1.72 million submissions with pass/fail labels to examine the accuracy of these test cases in evaluation. The results indicated that CodeContests+ achieves significantly higher accuracy than CodeContests, particularly with a notably higher True Positive Rate (TPR). Subsequently, our experiments in LLM Reinforcement Learning (RL) further confirmed that improvements in test case quality yield considerable advantages for RL.

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