ASE2024

LLMs and Prompting for Unit Test Generation: A Large-Scale Evaluation

Wendkûuni C. Ouédraogo, Abdoul Kader Kaboré, Haoye Tian, Yewei Song, Anil Koyuncu, Jacques Klein, David Lo, Tegawendé F. Bissyandé

被引用 8 次

摘要

Unit testing, essential for identifying bugs, is often neglected due to time constraints. Automated test generation tools exist but typically lack readability and require developer intervention. Large Language Models (LLMs) like GPT and Mistral show potential in test generation, but their effectiveness remains unclear.