Abstract
Extracting conceptual models from natural language requirements can help identify dependencies, redundancies, and conflicts between requirements via a holistic and easy-to-understand view that is generated from lengthy textual specifications. Unfortunately, existing approaches never gained traction in practice, because they either require substantial human involvement or they deliver too low accuracy. In this paper, we propose an automated approach called Visual Narrator based on natural language processing that extracts conceptual models from user story requirements. We choose this notation because of its popularity among (agile) practitioners and its focus on the essential components of a requirement: Who? What? Why? Coupled with a careful selection and tuning of heuristics, we show how Visual Narrator enables generating conceptual models from user stories with high accuracy. Visual Narrator is part of the holistic Grimm method for user story collaboration that ranges from elicitation to the interactive visualization and analysis of requirements.
Highlights
The software industry commonly uses natural language (NL) notations to express software requirements [47], with NL being employed by over 60% of practitioners [32]
We propose an automated approach called Visual Narrator based on natural language processing that extracts conceptual models from user story requirements
In previous work [53], we have shown the feasibility of this recipe by introducing the Visual Narrator tool for extracting conceptual models from user stories via NLP
Summary
The software industry commonly uses natural language (NL) notations to express software requirements [47], with NL being employed by over 60% of practitioners [32]. With the increasing adoption of agile development practices such as Scrum, the semi-structured NL notation of user stories is gaining momentum [31, 38]. NL requirements are easy to understand because they employ the very same language that we use to communicate with others. The ambiguity of words and sentences is a well-known and widely studied problem that results in different interpretations of the same text. We focus on another difficult problem: the identification and exploration of the key entities and relationships in a large set of requirements. Our work is intended to support the detection of dependencies between requirements, redundancies, and inconsistencies
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