Causal relations (If A, then B) are prevalent in requirements artifacts. Automatically extracting causal relations from requirements holds great potential for various RE activities (e.g., automatic derivation of suitable test cases). However, we lack an approach capable of extracting causal relations from natural language with reasonable performance. In this paper, we present our tool CATE (CAusality Tree Extractor), which is able to parse the composition of a causal relation as a tree structure. CATE does not only provide an overview of causes and effects in a sentence, but also reveals their semantic coherence by translating the causal relation into a binary tree. We encourage fellow researchers and practitioners to use CATE at https://causalitytreeextractor.com/

中文翻译：

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CATE：从自然语言需求中提取因果关系树

因果关系（如果 A，则 B）在需求工件中很普遍。从需求中自动提取因果关系对于各种 RE 活动（例如，合适测试用例的自动推导）具有巨大的潜力。然而，我们缺乏一种能够以合理的性能从自然语言中提取因果关系的方法。在本文中，我们展示了我们的工具 CATE（因果关系树提取器），它能够将因果关系的组成解析为树结构。CATE 不仅提供了一个句子中因果关系的概述，而且通过将因果关系翻译成二叉树来揭示它们的语义连贯性。我们鼓励其他研究人员和从业人员在 https://causalitytreeextractor.com/ 上使用 CATE