Automated unit test generators, particularly searchbased software testing tools like EvoSuite, are capable of generating tests with high coverage. Although these generators alleviate the burden of writing unit tests, they often pose challenges for software engineers in terms of understanding the generated tests. To address this, we introduce UTGen, which combines searchbased software testing and large language models to enhance the understandability of automatically generated test cases. We achieve this enhancement through contextualizing test data, improving identifier naming, and adding descriptive comments. Through a controlled experiment with 32 participants from both academia and industry, we investigate how the understandability of unit tests affects a software engineer's ability to perform bug-fixing tasks. We selected bug-fixing to simulate a real-world scenario that emphasizes the importance of understandable test cases. We observe that participants working on assignments with UTGen test cases fix up to 33 % more bugs and use up to 20 % less time when compared to baseline test cases. From the post-test questionnaire, we gathered that participants found that enhanced test names, test data, and variable names improved their bugfixing process.

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.

Automatic unit test generators such as EvoSuite are able to automatically generate unit test suites with high coverage. This removes the burden of writing unit tests from developers, but the generated tests are often difficult to understand for them. In this paper, we introduce the MicroTestCarver approach that generates unit tests starting from manual or scripted end-to-end (E2E) tests. Using carved information from these E2E tests, we generate unit tests that have meaningful test scenarios and contain actual test data. When we apply our MicroTestCarver approach, we observe that 85% of the generated tests are executable. Through a user study involving 20 participants, we get indications that tests generated with MicroTestCarver are relatively easy to understand.