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- W4387123276 abstract "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., “If A or B then C.”) imply causal relationships which—when extracted—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% and correct variable configurations in 92.3% of all cases, which corroborates the feasibility of our approach." @default.
- W4387123276 created "2023-09-29" @default.
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- W4387123276 date "2023-09-01" @default.
- W4387123276 modified "2023-10-15" @default.
- W4387123276 title "CiRA: An Open-Source Python Package for Automated Generation of Test Case Descriptions from Natural Language Requirements" @default.
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- W4387123276 doi "https://doi.org/10.1109/rew57809.2023.00019" @default.
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