Domain Knowledge is All You Need: A Field Deployment of LLM-Powered Test Case Generation in FinTech Domain

Zhiyi Xue, Liangguo Li, Senyue Tian, Xiaohong Chen, Pingping Li, Liangyu Chen, Tingting Jiang, Min Zhang

Abstract: Despite the promise of automation, general-purpose Large Language Models (LLMs) face difficulties in generating complete and accurate test cases from informal software requirements, primarily due to challenges in interpreting unstructured text and producing diverse, relevant scenarios. This paper argues that incorporating domain knowledge significantly improves LLM performance in test case generation. We report on the successful deployment of our LLM-powered tool, LLM4Fin, in the FinTech domain, showcasing the crucial role of domain knowledge in addressing the aforementioned challenges. We demonstrate two methods for integrating domain knowledge: implicit incorporation through model fine-tuning, and explicit incorporation with algorithm design. This combined approach delivers remarkable results, achieving up to 98.18% improvement in test scenario coverage and reducing generation time from 20 minutes to 7 seconds.