Natural Language Requirements Processing: A 4D Vision

The future evolution of the application of natural language processing technologies in requirements engineering can be viewed from four dimensions: discipline, dynamism, domain knowledge, and datasets

Natural language processing for requirements engineering: The best is yet to come

As part of the growing interest in natural language processing for requirements engineering (RE), RE researchers, computational linguists, and industry practitioners met at the First Workshop on Natural Language Processing for Requirements Engineering (NLP4RE 18). This article summarizes the workshop and presents an overview of the discussion held on the field’s future. This article is part of a theme issue on software engineering’s 50th anniversary.Postprint (author's final draft

The REVERE project:Experiments with the application of probabilistic NLP to systems engineering

Despite natural language’s well-documented shortcomings as a medium for precise technical description, its use in software-intensive systems engineering remains inescapable. This poses many problems for engineers who must derive problem understanding and synthesise precise solution descriptions from free text. This is true both for the largely unstructured textual descriptions from which system requirements are derived, and for more formal documents, such as standards, which impose requirements on system development processes. This paper describes experiments that we have carried out in the REVERE1 project to investigate the use of probabilistic natural language processing techniques to provide systems engineering support

AUTOMATED ANALYSIS OF NATURAL-LANGUAGE REQUIREMENTS USING NATURAL LANGUAGE PROCESSING

Natural Language (NL) is arguably the most common vehicle for specifying requirements. This dissertation devises automated assistance for some important tasks that requirements engineers need to perform in order to structure, manage, and elaborate NL requirements in a sound and effective manner. The key enabling technology underlying the work in this dissertation is Natural Language Processing (NLP). All the solutions presented herein have been developed and empirically evaluated in close collaboration with industrial partners. The dissertation addresses four different facets of requirements analysis: • Checking conformance to templates. Requirements templates are an effective tool for improving the structure and quality of NL requirements statements. When templates are used for specifying the requirements, an important quality assurance task is to ensure that the requirements conform to the intended templates. We develop an automated solution for checking the conformance of requirements to templates. • Extraction of glossary terms. Requirements glossaries (dictionaries) improve the understandability of requirements, and mitigate vagueness and ambiguity. We develop an auto- mated solution for supporting requirements analysts in the selection of glossary terms and their related terms. • Extraction of domain models. By providing a precise representation of the main concepts in a software project and the relationships between these concepts, a domain model serves as an important artifact for systematic requirements elaboration. We propose an automated approach for domain model extraction from requirements. The extraction rules in our approach encompass both the rules already described in the literature as well as a number of important extensions developed in this dissertation. • Identifying the impact of requirements changes. Uncontrolled change in requirements presents a major risk to the success of software projects. We address two different dimen- sions of requirements change analysis in this dissertation: First, we develop an automated approach for predicting how a change to one requirement impacts other requirements. Next, we consider the propagation of change from requirements to design. To this end, we develop an automated approach for predicting how the design of a system is impacted by changes made to the requirements

Designing a Requirement Mining System

The success of information systems (IS) development strongly depends on the accuracy of the requirements gathered from users and other stakeholders. When developing a new IS, about 80 percent of these requirements are recorded in informal requirements documents (e.g., interview transcripts or discussion forums) using natural language. However, processing the resultant natural language requirements resources is inherently complex and often error prone due to ambiguity, inconsistency, and incompleteness. Thus, even highly qualified requirements engineers often struggle to process large amounts of natural language requirements resources efficiently and effectively. In this paper, we propose a design theory for requirement mining systems (RMSs) based on two design principles: (1) semi-automatic requirement mining and (2) usage of imported and retrieved knowledge. As part of an extensive design project, which led to these principles, we also implemented a prototype based on this design theory (REMINER). It supports requirements engineers in identifying and classifying requirements documented in natural language and allows us to evaluate the artifact’s viability and the conceptual soundness of our design. The results of our evaluation suggest that an RMS based on our proposed design principles can significantly improve recall while maintaining precision levels