LoRA-Based Fine-Tuning of LLMs for Automated Test Case Generation from Natural Language Requirements
arXiv·medium signal
Evaluates LoRA fine-tuning approaches for generating structured, executable test cases from natural language requirements — a persistent challenge in software engineering due to requirement ambiguity. The study benchmarks multiple LLM architectures with LoRA against full fine-tuning and zero-shot baselines, finding that LoRA achieves 85-92% of full fine-tuning quality at a fraction of compute cost. Directly applicable for teams building test automation from specs.