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Requirements often specify the expected system behavior by using causalrelations (e.g., If A, then B). Automatically extracting these relationssupports, among others, two prominent RE use cases: automatic test casederivation and dependency detection between requirements. However, existingtools fail to extract causality from natural language with reasonableperformance. In this paper, we present our tool CiRA (Causality detection inRequirements Artifacts), which represents a first step towards automaticcausality extraction from requirements. We evaluate CiRA on a publiclyavailable data set of 61 acceptance criteria (causal: 32; non-causal: 29)describing the functionality of the German Corona-Warn-App. We achieve a macroF_1 score of 83%, which corroborates the feasibility of our approach.

CiRA: A Tool for the Automatic Detection of Causal Relationships in Requirements Artifacts