Papers with test case construction
CodeContests-O: Powering LLMs via Feedback-Driven Iterative Test Case Generation (2026.findings-acl)
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Jianfeng Cai, Jinhua Zhu, Ruopei Sun, Kangwen Zhao, Dongyun Xue, Mingxiao Feng, Wengang Zhou, Houqiang Li
| Challenge: | Existing approaches to synthesize test cases using Large Language Models (LLMs) rely on the model’s intrinsic generation capabilities without external feedback, resulting in insufficiently diverse cases. |
| Approach: | They propose a feedback-driven iterative framework that leverages Large Language Models to generate initial test cases, execute them against known correct and incorrect solutions, and utilizes the failed results as feedback to guide the LLM in refining the test cases toward high fidelity and discriminability. |
| Outcome: | The proposed method outperforms the existing codecontests and codecontests+ models by 4.30% and 8.78%. |
NaturalCodeBench: Examining Coding Performance Mismatch on HumanEval and Natural User Queries (2024.findings-acl)
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| Challenge: | Large language models (LLMs) generate code for productive activities, but current benchmarks for code synthesis are oriented towards introductory tasks on algorithm and data science. |
| Approach: | They propose a code benchmark to mirror the complexity and variety of scenarios in real-world coding tasks. |
| Outcome: | The proposed benchmark improves on 39 large language models with close HumanEval scores and achieves an efficiency increase of more than 4 times. |