LogiCase: Effective Test Case Generation from Logical Description in Competitive Programming
This work addresses the challenge of automated test case generation for competitive programming, providing a scalable framework to improve algorithm assessments, though it is incremental as it builds on existing grammar and translation methods.
The paper tackled the problem of generating effective test cases from logical descriptions in competitive programming by introducing Context-Free Grammars with Counters (CCFGs) and using a fine-tuned CodeT5 model, resulting in significant gains in validity and effectiveness over baseline methods on the CodeContests dataset.
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.