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

TL;DR

This paper introduces CiRA, an NLP-based tool that automatically generates acceptance test cases from natural language requirements by extracting conditionals, demonstrated through a case study with industry partners.

Contribution

The paper presents CiRA, a novel NLP approach for automatically deriving acceptance test cases from informal requirements, addressing a gap in existing semi-formal requirement analysis tools.

Findings

71.8% of manually created test cases were automatically generated by CiRA

CiRA discovered 80 relevant test cases missed in manual design

Feasibility demonstrated through a case study with industry partners

Abstract

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/.