AI-Driven Test Case Generation from Natural Language Requirements: A Survey of Techniques and Research Gaps
arXiv:2606.06563v1 Announce Type: cross Abstract: 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
The rapid advancements in AI, particularly Large Language Models (LLMs), have made automated test case generation from natural language requirements increasingly feasible, pushing this research area forward.
Automating test case generation directly from natural language requirements can significantly reduce software development costs and time, improving software quality and accelerating innovation cycles.
The barrier to entry for developing complex, high-quality software is lowered by automating a critical and costly phase, potentially enabling faster deployment of new technologies and features.
- · Software Development Companies
- · AI/LLM Developers
- · Quality Assurance Sector
- · Any Industry reliant on Software
- · Manual Test Case Specialists
- · Companies with Legacy Software Testing Processes
Significant reduction in software development and testing timelines and costs as AI automates a labor-intensive process.
Improved software reliability and security across all sectors due to more comprehensive and efficient testing.
Accelerated innovation cycles across various industries as software development becomes faster and more agile, leading to new products and services.
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Read at arXiv cs.AI