An initial investigation of ChatGPT unit test generation capability

• Vitor Guilherme UFSCar
• Auri Vincenzi UFSCar

Resumo

Context: Software testing ensures software quality, but developers often disregard it. The use of automated testing generation is pursued to reduce the consequences of overlooked test cases in a software project. Problem: In the context of Java programs, several tools can completely automate generating unit test sets. Additionally, studies are conducted to offer evidence regarding the quality of the generated test sets. However, it is worth noting that these tools rely on machine learning and other AI algorithms rather than incorporating the latest advancements in Large Language Models (LLMs). Solution: This work aims to evaluate the quality of Java unit tests generated by an OpenAI LLM algorithm, using metrics like code coverage and mutation test score. Method: For this study, 33 programs used by other researchers in the field of automated test generation were selected. This approach was employed to establish a baseline for comparison purposes. For each program, 33 unit test sets were generated automatically, without human interference, by changing Open AI API parameters. After executing each test set, metrics such as code line coverage, mutation score, and success rate of test execution were collected to evaluate the efficiency and effectiveness of each set. Summary of Results: Our findings revealed that the OpenAI LLM test set demonstrated similar performance across all evaluated aspects compared to traditional automated Java test generation tools used in the previous research. These results are particularly remarkable considering the simplicity of the experiment and the fact that the generated test code did not undergo human analysis.

Palavras-chave: automated test generation, coverage testing, experimental software engineering, mutation testing, software testing, testing tools