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

• 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. © 2023 IEEE.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Software Engineering (hsv//eng)

Nyckelord

acceptance test

BERT

natural language processing

requirements engineering

test case description

Computer software selection and evaluation

High level languages

Natural language processing systems

Open source software

Open systems

Case description

Language processing

Natural language requirements

Natural languages

Open-source

Requirement engineering

Test case

Acceptance tests

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