CATE: CAusality Tree Extractor from Natural Language Requirements
This addresses the need for automated causal relation extraction in requirements engineering, offering a practical tool for researchers and practitioners.
The paper tackles the problem of automatically extracting causal relations from natural language requirements, presenting the tool CATE that parses these relations into tree structures, achieving reasonable performance for RE activities like test case derivation.
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/