# A Tool for Test Case Scenarios Generation Using Large Language Models

arXiv: https://arxiv.org/abs/2406.07021 · HTML: https://mlsanity.com/paper/2406.07021/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2024.

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

- [A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards](https://mlsanity.com/paper/2505.13766/) (2025)
- [Supporting software engineering tasks with agentic AI: Demonstration on document retrieval and test scenario generation](https://mlsanity.com/paper/2602.04726/) (2026)
- [PPO guided Agentic Pipeline for Adaptive Prompt Selection and Test Case Generation](https://mlsanity.com/paper/2605.00942/) (2026)
- [FeedbackLLM: Metadata driven Multi-Agentic Language Agnostic Test Case Generator with Evolving prompt and Coverage Feedback](https://mlsanity.com/paper/2605.01264/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) are widely used in Software Engineering (SE) for various tasks, including generating code, designing and documenting software, adding code comments, reviewing code, and writing test scripts. However, creating test scripts or automating test cases demands test suite documentation that comprehensively covers functional requirements. Such documentation must enable thorough testing within a constrained scope and timeframe, particularly as requirements and user demands evolve. This article centers on generating user requirements as epics and high-level user stories and crafting test case scenarios based on these stories. It introduces a web-based software tool that employs an LLM-based agent and prompt engineering to automate the generation of test case scenarios against user requirements.