Workshop on Natural Language Processing for Requirements Engineering (NLP4RE'18)

Abstract

Natural language processing (NLP) has played an important role in several computer science areas, and requirements engineering (RE) is not an exception. For over twenty years, several works were published on the application of NLP techniques to address RE speci c problems, such as traceability, categorisation, defect detection, model generation, and more. In recent years, the advent of massive and heterogeneous natural language (NL) RE-relevant sources, like tweets and app reviews, has sparked the interest of the RE community in NLP. Furthermore, we witness the novel golden age of NLP technologies, enabled by deep and shallow learning approaches that have improved the accuracy of most NLP tasks, including parsing and machine translation. It is therefore an appropriate moment to create a venue in which researchers on applications of NLP to RE problems can meet, share ideas and create synergies, assisted by experts from the NLP community.

Preface

Technical Papers on RE Needs and NLP Solutions

Groen et al. [GSK+] focus on RE needs, and present a paper aimed at establishing whether NLP is actually necessary in RE. They investigate the problem of extracting requirements-relevant information from the large amounts of available online user feedback about software products such as apps, and they compare the amount of time required for conducting a manual versus automated analysis. They conclude that automated analysis is signi cantly faster, con rming the need for NLP in RE. Schlutter and Vogelsang [SV] focus on RE solutions instead, and they propose a method to ease requirements comprehension. The method collects disjoint requirements belonging to di erent components of a larger system, and automatically combines them into a single knowledge representation graph that relates the requirements. The coherent view on requirements concepts and relations provided by the graph can be used to support requirements comprehension and analysis. 1.2

Reports from Research Groups

Report papers from research groups, particularly encouraged by the call for papers of NLP4RE'18, form the large majority of the accepted contributions. Three reports come from within the RE research community [GF, BCFQ, FSM+], three reports belong to the arti cial intelligence and NLP community [TH, BG, Tsa], and one report presents experiences from a company [Fem].

Gnesi and Ferrari [GF] present the past and current work of the FMT lab at CNR-ISTI on the usage of NLP for defect detection in requirements, support for product lines' synthesis, and analysis of requirements elicitation interviews. Borull et al. [BCFQ] report on the ongoing work of the GESSI group at UPC within the context of the OpenReq EU project, focussing on the identi cation of similar requirements, to support interdependency detection, and reuse. Fucci et al. [FSM+] present the work of the MAST group at the University of Hamburg, mostly dedicated to the automated analysis of online user feedback and app reviews for the extraction of requirements-relevant information.

Tsarfaty [Tsa], from the NLP lab at the Open University of Israel, presents the work of the group performed in the context of the ERC-StG research grant named Natural Language Programming (NLPRO): Turning Texts into Executable Code. The project aims at developing a novel natural language compiler that takes a natural language description of a system as input, and returns as output the respective executable. Tows and Heuss [TH] report on the research done at Fraunhofer FKIE concerning requirements grouping by means of di erent similarity evaluation techniques. Baumer and Geierhos [BG], from the department of Digital Humanities at the University of Paderborn, present the ongoing work of their group concerning the development of the CORDULA platform, oriented to recognise and automatically compensate language inaccuracies (e.g., ambiguity, vagueness and incompleteness) in requirements.

Femmer [Fem] presents the only company report at the workshop, which introduces the Qualicen Requirements Scout, a commercial tool for defect detection in requirements. The author illustrates the initial research performed at TUM that led to the development of the tool, and outlines research outcomes and industrial challenges from his perspective of tool provider. 1.3

Vision Papers

Garigliano et al. [GPM] present a vision paper that argues that NLP tools for general RE should be focused on a deep internal semantic representation of the text, which attempts to describe the text meaning in a form that can be di erent from the original one. This is expected to enable the extraction of implied information that is not explicitely given in the text. Friesen at al. [FBG], instead, present the idea of improving requirements quality by using chatbot technologies. Chatbots are expected to support the automatic compensation of some de cits in natural language requirement descriptions, by means of direct and guided interaction with the user. 1.4

Posters

Kifetew et al. [KPS] present a poster discussing the lessons learned in the SUPERSEDE EU project, aimed at processing user feedback to support software evolution. Speci cally, the authors discuss issues related to the analysis of feedback that was made available in German, rather than English, and the consequent need for adaptation of available NLP approaches, mostly tailored for English. Caron et al. [CBG] present the idea for a system aimed at extracting information from requirements with a syntactic approach, and they outline the architecture for such system. Gori et al. [GOP+] presents an experiment that uses machine learning to detect requirements defects, and they illustrate the encountered problems and challenges. This last poster does not appear in the current proceedings. 2

Program Committee

We warmly thank all the reviewers of our Program Committee (PC), who helped in the selection of the papers by providing timely and accurate reviews. The PC members of NLP4RE are:

Daniel M. Berry, University of Waterloo, Canada

Jorg Dorr, Fraunhofer IESE, Germany

Henning Femmer, Technical University of Munich, Germany

Davide Fucci, University of Hamburg, Germany

Vincenzo Gervasi, University of Pisa, Italy

Eduard Groen, Fraunhofer IESE, Germany

Emitza Guzman, University of Zurich, Switzerland

Garm Lucassen, Utrecht University, the Netherlands

Daniel Mendez, Technical University of Munich, Germany

Luisa Mich, University of Trento, Italy

Barbara Paech, University of Heidelberg, Germany

Mehrdad Sabetzadeh, University of Luxembourg, Luxembourg

Nicolas Sannier, University of Luxembourg, Luxembourg

Pete Sawyer, Aston University, UK

Norbert Sey , University of Zurich and University of Applied Sciences and Arts Northwestern, Switzerland

Michael Unterkalmsteiner, Blekinge Institute of Technology, Sweden

Andreas Vogelsang, TU Berlin, Germany

[BCFQ] Ricard Borrull, Dolors Costal, Xavier Franch, and Carme Quer. Research on NLP for RE at UPC: a

Report.

Stefania Gnesi and Alessio Ferrari. Research on NLP for RE at CNR-ISTI: a Report. [GOP+] Gloria Gori, Francesco Orsini, Marco Papini, Alessandro Fantechi, and Paolo Frasconi. Defect Detection and Machine Learning for Requirement Engineering: new Roadmaps. [GPM]

Roberto Garigliano, Dominic Perini, and Luisa Mich. Which Semantics for Requirements Engineering: from Shallow to Deep. [KPS] [SV] [TH]

Fitsum Meshesha Kifetew, Anna Perini, and Angelo Susi. Managing Multi-Lingual User Feedback: the SUPERSEDE project experience.

Aaron Schlutter and Andreas Vogelsang. Knowledge Representation of Requirements Documents Using Natural Language Processing.

Daniel Tows and Timm Heuss. Research on NLP for RE at Fraunhofer FKIE: a Report on Grouping Requirements. [Tsa]

Reut Tsarfaty. Natural Language Programming (NLPRO): Turning Texts into Code.

References