# Requirements-Based Test Generation: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2505.02015 · HTML: https://mlsanity.com/paper/2505.02015/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2025.

## Built on by (most cited first, 4 of 4)

- [AI-Driven Test Case Generation from Natural Language Requirements: A Survey of Techniques and Research Gaps](https://mlsanity.com/paper/2606.06563/) (2026)
- [An empirical study of LoRA-based fine-tuning of large language models for automated test case generation](https://mlsanity.com/paper/2604.06946/) (2026)
- [RESTestBench: A Benchmark for Evaluating the Effectiveness of LLM-Generated REST API Test Cases from NL Requirements](https://mlsanity.com/paper/2604.25862/) (2026)
- [Supporting System Testing with a Multi-Agent LLM-based Framework for Knowledge Graph Extraction: A Case Study with Ethernet Switch Systems](https://mlsanity.com/paper/2605.19180/) (2026)

## Abstract (arXiv)

As an important way of assuring software quality, software testing generates and executes test cases to identify software failures. Many strategies have been proposed to guide test-case generation, such as source-code-based approaches and methods based on bug reports. Requirements-based test generation (RBTG) constructs test cases based on specified requirements, aligning with user needs and expectations, without requiring access to the source code. Since its introduction in 1994, there have been many contributions to the development of RBTG, including various approaches, implementations, tools, assessment and evaluation methods, and applications. This paper provides a comprehensive survey on RBTG, categorizing requirement types, classifying approaches, investigating types of test cases, summarizing available tools, and analyzing experimental evaluations. This paper also summarizes the domains and industrial applications of RBTG, and discusses some open research challenges and potential future work.