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Deriving acceptance tests from high-level, natural language requirements that achieve full coverage is a major manual challenge at the interface between requirements engineering and testing. Conditional requirements (e.g., &#x201C;If A or B then C.&#x201D;) imply causal relationships which&#x2014;when extracted&#x2014;allow to generate these acceptance tests automatically. This paper presents a tool from the CiRA (Causality In Requirements Artifacts) initiative, which automatically processes conditional natural language requirements and generates a minimal set of test case descriptions achieving full coverage. We evaluate the tool on a publicly available data set of 61 requirements from the requirements specification of the German Corona-Warn-App. The tool infers the correct test variables in 84.5&#x0025; and correct variable configurations in 92.3&#x0025; of all cases, which corroborates the feasibility of our approach.

CiRA: An Open-Source Python Package for Automated Generation of Test Case Descriptions from Natural Language Requirements