LogiCase: Effective Test Case Generation from Logical Description in Competitive Programming
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913849948504064 |
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| author | Sung, Sicheol Aditi kim, Dogyu Han, Yo-Sub Ko, Sang-Ki |
| author_facet | Sung, Sicheol Aditi kim, Dogyu Han, Yo-Sub Ko, Sang-Ki |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_15039 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LogiCase: Effective Test Case Generation from Logical Description in Competitive Programming Sung, Sicheol Aditi kim, Dogyu Han, Yo-Sub Ko, Sang-Ki Software Engineering Artificial Intelligence 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. |
| title | LogiCase: Effective Test Case Generation from Logical Description in Competitive Programming |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2505.15039 |