Fine-Tuning Foundation Models for Domain-Specific Test Case Generation

Abstract

Domain-specific fine-tuning has become a cornerstone technique for adapting pre-trained models to high-stakes and specialized tasks across sectors such as healthcare, law, finance, and software engineering. This process customizes foundational AI models by exposing them to domain-relevant data, optimizing their performance for use cases that require nuanced understanding, strict compliance, or specialized syntax. In this article, we examine the theoretical foundation of domain-specific fine-tuning, reinforced by real-world case studies across medical imaging, legal NLP, financial sentiment analysis, and code generation. Additionally, we investigate its emerging role in automated test case generation using large language models (LLMs), demonstrating how fine-tuned models can enhance software quality by producing context-aware, risk-prioritized, and regulation-compliant test cases. By analyzing different fine-tuning techniques, we identify critical considerations including data representativeness, risk of overfitting, and continuous learning. The results underscore the transformative potential of fine-tuning in making AI models both reliable and valuable for domain-specific deployment.