AI-Driven Test Case Generation from Natural Language Requirements: A Survey of Techniques and Research Gaps

2026-06-04 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, quick

Summary

A recent survey, submitted on June 4, 2026, analyzes AI-driven test case generation from natural language requirements, a critical yet expensive software development activity. It systematically reviewed 21 primary studies published between 2000 and 2025, following Kitchenham and Charters' guidelines. The research organizes the literature into three evolutionary eras, revealing that no existing approach simultaneously satisfies six key quality dimensions: automation, ambiguity handling, domain applicability, traceability, evaluation thoroughness, and hallucination control. The survey contributes a three-era synthesis, a six-criteria gap analysis, and four actionable research guidelines targeting hallucination, traceability, complexity sensitivity, and compliance.

Key takeaway

For software engineers and QA leads evaluating AI tools for test case generation, understand that current AI/NLP/LLM approaches introduce risks like hallucination and reduced traceability. No single solution fully satisfies critical quality dimensions such as automation, ambiguity handling, and evaluation thoroughness. You must carefully assess potential tools against these six criteria and prioritize solutions that specifically address hallucination, traceability, and complexity sensitivity to mitigate risks effectively.

Key insights

Automating test case generation from natural language requirements with AI faces significant challenges, particularly in quality dimensions like hallucination and traceability.

Principles

• Software testing is a critical, costly development phase.
• Natural language requirements introduce ambiguity for test generation.
• No current AI approach fully meets six key quality dimensions.

Method

The survey followed Kitchenham and Charters' systematic review guidelines, analyzing 21 primary studies from 2000-2025 to synthesize AI-based test generation techniques.

In practice

• Prioritize research on AI hallucination control.
• Enhance traceability in AI-generated test cases.
• Develop solutions for complexity sensitivity.

Topics

• Software Testing
• Test Case Generation
• Natural Language Processing
• Large Language Models
• Requirements Engineering
• AI in Software Engineering
• Systematic Review

Best for: AI Scientist, Research Scientist, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.