An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation

Unit tests play a key role in ensuring the correctness of software. However, manually creating unit tests is a laborious task, motivating the need for automation. Large Language Models (LLMs) have recently been applied to various aspects of software development, including their suggested use for automated generation of unit tests, but while requiring additional training or few-shot learning on examples of existing tests. This paper presents a large-scale empirical evaluation on the effectiveness of LLMs for automated unit test generation without requiring additional training or manual effort. Concretely, we consider an approach where the LLM is provided with prompts that include the signature and…

• CR
  Canada Research Chairs
• NS
  Natural Sciences and Engineering Research Council of Canada

  Award: RGPIN-2017-04289