Fine-Grained Causality Extraction From Natural Language Requirements Using Recursive Neural Tensor Networks

TL;DR

This paper introduces a novel approach using Recursive Neural Tensor Networks to extract fine-grained causal relations from natural language requirements, supported by a new labeled dataset and achieving promising accuracy for automated test case generation.

Contribution

It presents the first fully labeled causality Treebank and a neural network-based method for detailed causality extraction from natural language requirements.

Findings

Achieved 74% F1 score on causality extraction

Created the first causality Treebank with 1,571 labeled trees

Provided open datasets and code for community use

Abstract

[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.