AI-Driven Test Case Generation from Natural Language Requirements: A Survey of Techniques and Research Gaps
A new survey paper published on arXiv details the current state of AI-driven test case generation from natural language requirements. The research synthesizes 21 studies from 2000-2025, identifying three evolutionary eras in the field. It highlights that no existing approach fully addresses key quality dimensions such as automation, ambiguity handling, traceability, and hallucination control. The survey concludes by proposing four research guidelines to address these remaining gaps. AI
IMPACT Identifies critical gaps in AI-driven software testing, guiding future research towards more robust and reliable automated solutions.