Feb. 7, 2024, 5:43 a.m. | Yifeng He Jiabo Huang Yuyang Rong Yiwen Guo Ethan Wang Hao Chen

cs.LG updates on arXiv.org arxiv.org

The remarkable capability of large language models (LLMs) in generating high-quality code has drawn increasing attention in the software testing community. However, existing code LLMs often demonstrate unsatisfactory capabilities in generating accurate and complete tests since they were trained on code snippets collected without differentiating between code for testing purposes and other code. In this paper, we present a large-scale dataset UniTSyn, which is capable of enhancing the prowess of LLMs for Unit Test Synthesis. Associating tests with the tested …

attention capabilities capability code code llms community cs.ai cs.cl cs.cr cs.lg cs.se dataset language language models large language large language models llms quality scale software software testing testing tests

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