A spaCy-based tool for extracting variability from NL requirements

Abstract

In previous work, we have shown that ambiguity detection in requirements can also be used as a way to capture latent aspects of variability. Natural Language Processing (NLP) tools have been used for a lexical analysis aimed at ambiguity indicators detection, and we have studied the necessary adaptations to those tools for pointing at potential variability, essentially by adding specific dictionaries for variability. We have identified also some syntactic rules able to detect potential variability, such as disjunction between nouns or pairs of indicators in a subordinate proposition. This paper describes a new prototype NLP tool, based on the spaCy library, specifically designed to detect variability. The prototype is shown to preserve the same recall exhibited by previously used lexical tools, with a higher precision.