  [Context:] Causal relations (e.g., If A, then B) are prevalent in functional
requirements. For various applications of AI4RE, e.g., the automatic derivation
of suitable test cases from requirements, automatically extracting such causal
statements are a basic necessity. [Problem:] We lack an approach that is able
to extract causal relations from natural language requirements in fine-grained
form. Specifically, existing approaches do not consider the combinatorics
between causes and effects. They also do not allow to split causes and effects
into more granular text fragments (e.g., variable and condition), making the
extracted relations unsuitable for automatic test case derivation. [Objective &
Contributions:] We address this research gap and make the following
contributions: First, we present the Causality Treebank, which is the first
corpus of fully labeled binary parse trees representing the composition of
1,571 causal requirements. Second, we propose a fine-grained causality
extractor based on Recursive Neural Tensor Networks. Our approach is capable of
recovering the composition of causal statements written in natural language and
achieves a F1 score of 74 % in the evaluation on the Causality Treebank. Third,
we disclose our open data sets as well as our code to foster the discourse on
the automatic extraction of causality in the RE community.
