# Automatic Creation of Acceptance Tests by Extracting Conditionals from Requirements: NLP Approach and Case Study

arXiv: https://arxiv.org/abs/2202.00932 · HTML: https://mlsanity.com/paper/2202.00932/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2022.

## Built on by (most cited first, 3 of 3)

- [Automated structural testing of LLM-based agents: methods, framework, and case studies](https://mlsanity.com/paper/2601.18827/) (2026)
- [AI-Driven Test Case Generation from Natural Language Requirements: A Survey of Techniques and Research Gaps](https://mlsanity.com/paper/2606.06563/) (2026)
- [AI-driven Software Development: A Pragmatic Path to Agentic Development Processes](https://mlsanity.com/paper/2606.15283/) (2026)

## Abstract (arXiv)

Acceptance testing is crucial to determine whether a system fulfills end-user requirements. However, the creation of acceptance tests is a laborious task entailing two major challenges: (1) practitioners need to determine the right set of test cases that fully covers a requirement, and (2) they need to create test cases manually due to insufficient tool support. Existing approaches for automatically deriving test cases require semi-formal or even formal notations of requirements, though unrestricted natural language is prevalent in practice. In this paper, we present our tool-supported approach CiRA (Conditionals in Requirements Artifacts) capable of creating the minimal set of required test cases from conditional statements in informal requirements. We demonstrate the feasibility of CiRA in a case study with three industry partners. In our study, out of 578 manually created test cases, 71.8 % can be generated automatically. Additionally, CiRA discovered 80 relevant test cases that were missed in manual test case design. CiRA is publicly available at www.cira.bth.se/demo/.