Proceedings

LogiCase: Effective Test Case Generation from Logical Description in Competitive Programming

Sicheol Sung, Aditi, Dogyu Kim, Yo-Sub Han, Sang-Ki Ko

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 7742-7750.

https://doi.org/10.24963/ijcai.2025/861

Keywords

  • Natural Language Processing: NLP: Language models
  • Multidisciplinary Topics and Applications: MTA: Software engineering

Abstract

Automated Test Case Generation (ATCG) is crucial for evaluating software reliability, particularly in competitive programming where robust algorithm assessments depend on diverse and accurate test cases. However, existing ATCG methods often fail to meet complex specifications or generate effective corner cases, limiting their utility. In this work, we introduce Context-Free Grammars with Counters (CCFGs), a formalism that captures both syntactic and semantic structures in input specifications. Using a fine-tuned CodeT5 model, we translate natural language input specifications into CCFGs, enabling the systematic generation of high-quality test cases. Experiments on the CodeContests dataset demonstrate that CCFG-based test cases outperform baseline methods in identifying incorrect algorithms, achieving significant gains in validity and effectiveness. Our approach provides a scalable and reliable grammar-driven framework for enhancing automated competitive programming evaluations.

Sicheol Sung, Aditi, Dogyu Kim, Yo-Sub Han, and Sang-Ki Ko. 2025. LogiCase: Effective Test Case Generation from Logical Description in Competitive Programming. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 7742–7750. https://doi.org/10.24963/ijcai.2025/861
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