Using Large Language Models to Generate Concise and Understandable Test Case Summaries

Software testing is essential, and automatic test case generation can be an important aid to software engineers. However, generated tests are sometimes difficult to understand. Test summarization approaches that provide an overview of what exactly is tested can provide help, but existing summarization approaches generate documentation that is lengthy and redundant. In this paper, we investigate whether large language models (LLMs) can be used to generate more concise, yet understandable summaries. In a small-scale user study with 11 participants, we obtained positive feedback on the LLM-generated summaries.