Large Language Models for Unit Test Generation in React Native TypeScript Components

Sammanfattning

Advancements within Large Language Models(LLMs) have opened a world of opportunities within the software development domain. This thesis, through an controlled experiment, aims to investigate how LLMs can be utilized within software testing, more specifically unit testing. The controlled experiment was performed using a Python script interfacing with the gpt-3.5-turbo model, to automatically generate unit tests for React Native components written in TypeScript. The pipeline described, performs the calls to the OpenAI Application Programming Interface(API) iterative. To evaluate and retrieve the metric code coverage, the unit tests were executed with Jest. Additionally, manual execution of failing tests, both compilable and non-compilable tests were executed and the different kind of errors with their frequency were documented. The experiment shows that LLMs can be used to generate comprehensive and accurate unit tests, with high potential of future improvements. While the amount of generated tests that compiled were low, their nature was often good, failing because of easy correctable syntax errors, faulty imports or missing dependencies. The errors found, were at large part due to project configurations while others would probably be less frequent through more extensive prompt-engineering or by the use of an newer model. The experiment also shows that the temperature affected the outcome and that the type of errors were different between compiling and non-compiling tests. A lower temperature parameter to the OpenAI API generally achieved better results, whilst a higher temperature showed greater coverage at compiled failing tests. This thesis also shows that future opportunities and improvements are widely available. Through better project optimization, newer models and better prompting, a better result is to be expected. The script could with further development be turned into a working product, making software testing faster and more efficient, saving both time and money while simultaneously improving the test case quality.