AI-Driven Test Case Generation from Natural Language Requirements: A Survey of Techniques and Research Gaps
Software testing is critical for verifying that systems meet specified requirements, yet remains among the most time-consuming and expensive activities in development. Requirements-based test generation allows test cases to be derived early from requirements artifacts, but generating them directly from natural language is challenging due to inherent ambiguity and imprecision. Recent advances in AI, natural language processing (NLP), and large language models (LLMs) have made automating this pipeline increasingly feasible, while introducing new risks including hallucination, reduced traceability, and inconsistent evaluation. This survey addresses four research questions: what AI and NLP te...
| Authors | Orimoloye Folorunsho, Hassan Reza |
|---|---|
| Primary category | cs.SE |
| Published | 4 June 2026 |
| Last updated at source | 13 June 2026 |
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