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

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/.

Fundamental Research on Detecting Contradictions in Requirements: Taxonomy and Semi-Automated Approach

“…With our method, contradictions can be found in a semi-automated way: The classification into cause and effect, as well as variable and condition, are fully automated, for example by using Fischbach's parser [19]. This way our method can be applied automatically up to the step "Building Blocks".…”