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https://ceur-ws.org/Vol-2584/NLP4RE-preface.pdf
3rd Workshop on Natural Language Processing for
Requirements Engineering (NLP4RE’20)
Sallam Abualhaija Davide Fucci Fabiano Dalpiaz
University of Luxembourg Blekinge Institute of Technology Utrecht University
sallam.abualhaija@uni.lu davide.fucci@bth.se f.dalpiaz@uu.nl
Xavier Franch
Universitat Politècnica de Catalunya
franch@essi.upc.edu
1 Preface
Natural language processing (NLP) plays an important role in several areas of software engineering, and require-
ments engineering (RE) is not an exception. Requirements are generally authored and communicated in textual
form and in different levels of formality, from structured (e.g., user stories) to unstructured natural language.
Moreover, in the last few years, the advent of massive and heterogeneous sources, such as tweets and app reviews,
has attracted even more interest from the RE community, as demonstrated by the increasing number of scientific
papers on this topic in conferences like ICSE, RE, and REFSQ.
NLP has a long history in RE, in particular for providing automated solutions for quality assurance [DVdSL18],
inconsistency [GZ05], model extraction [ASBZ16] and more. In the recent years, the use of deep-learning al-
gorithms has introduced significant improvements in the accuracy of various NLP tasks like parsing and POS-
tagging—i.e., the ones that are used within RE applications. In this workshop, we aim at gathering people from
both communities (RE and NLP) in order to create opportunities for them to get inspired, communicate and
exchange ideas. RE presents several opportunities for the applied field of NLP, and NLP community shed the
light over the recent research advances.
This document is a preface to the proceedings of the 3rd Workshop on Natural Language Processing for Re-
quirements Engineering (NLP4RE’20, https://nlp4re.github.io/2020/reqeval.html), co-located with the
26th international Working Conference on Requirements Engineering: Foundation for Software Quality (REFSQ
2020) held in Pisa, Italy. After the first two successful editions [DFFP18a, DFF+ 19] (see summary of the first
edition at [DFFP18b]), the goal of NLP4RE’20 is to strengthen its role as a meeting point for the researchers
in the field, to foster collaborations, and to encourage synergies between industry, academia and vendors of
NLP tools for RE. The workshop features one keynote from Tristan Miller (Austrian Research Institute for
Artificial Intelligence) on the advances on Ambiguity research in NLP. The keynote provides a viewpoint on
the requirements and constraints for solving ambiguities and the recent advances in NLP to solve ambiguities
according to these constraints within the context of machine translation. The keynote provides an inspiration
for the RE community about incorporating domain knowledge in solving ambiguities. The workshop received
nine submissions. The papers were independently reviewed by three program committee members, and seven
papers were accepted for presentation at the workshop. The papers can be grouped into three main groups: (1)
technical papers discussing RE needs and associated NLP solutions [dB, WNT, JJT, MOU+, AFGS]; (2) report
papers presenting past, ongoing and future work of research groups interested in NLP for RE [BBDOF]; and (3)
tool demonstration papers [KC].
Copyright c 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC
BY 4.0).
In: M. Sabetzadeh, A. Vogelsang, S. Abualhaija, M. Borg, F. Dalpiaz, M. Daneva, N. Fernández, X. Franch, D. Fucci, V. Gervasi,
E. Groen, R. Guizzardi, A. Herrmann, J. Horkoff, L. Mich, A. Perini, A. Susi (eds.): Joint Proceedings of REFSQ-2020 Workshops,
Doctoral Symposium, Live Studies Track, and Poster Track, Pisa, Italy, 24-03-2020, published at http://ceur-ws.org
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’20 are:
• Frederik Simon Bäumer, Paderborn University, Germany
• Dan Berry, University of Waterloo, Canada
• Andrea Cimino, CNR-ISTI, Italy
• Jörg Dörr, Fraunhofer IESE, Germany
• Henning Femmer, Qualicen GmbH, Germany
• Vincenzo Gervasi, University of Pisa, Italy
• Sepideh Ghanavati, University of Maine, USA
• Eduard Groen, Fraunhofer IESE, Germany
• Emitzá Guzmán, Free University of Amsterdam, The Nederlands
• Frank Houdek, Daimler, Germany
• Lloyd Montgomery, University of Hamburg, Germany
• Barbara Paech, University of Heidelberg, Germany
• Mehrdad Sabetzadeh, University of Luxembourg, Luxembourg
• Nicolas Sannier, independent researcher
• Michael Unterkalmsteiner, Blekinge Institute of Technology, Sweden
• Andreas Vogelsang, TU Berlin, Germany
• Liping Zhao, University of Manchester, UK
References
[ASBZ16] Chetan Arora, Mehrdad Sabetzadeh, Lionel Briand, and Frank Zimmer. Extracting domain models
from natural-language requirements: approach and industrial evaluation. In Proceedings of the
ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems,
pages 250–260, 2016.
[DFF+ 19] Fabiano Dalpiaz, Alessio Ferrari, Xavier Franch, Sarah Gregory, Frank Houdek, and Cristina Palo-
mares. 2nd workshop on natural language processing for requirements engineering (nlp4re’19) and
nlp tool showcase. 2019.
[DFFP18a] Fabiano Dalpiaz, Alessio Ferrari, Xavier Franch, and Cristina Palomares. 1st workshop on natural
language processing for requirements engineering (nlp4re’18). 2018.
[DFFP18b] Fabiano Dalpiaz, Alessio Ferrari, Xavier Franch, and Cristina Palomares. Natural language process-
ing for requirements engineering: The best is yet to come. IEEE Software, 35(5):115–119, 2018.
[DVdSL18] Fabiano Dalpiaz, Ivor Van der Schalk, and Garm Lucassen. Pinpointing ambiguity and incomplete-
ness in requirements engineering via information visualization and nlp. In International Working
Conference on Requirements Engineering: Foundation for Software Quality, pages 119–135. Springer,
2018.
[GZ05] Vincenzo Gervasi and Didar Zowghi. Reasoning about inconsistencies in natural language require-
ments. ACM Transactions on Software Engineering and Methodology (TOSEM), 14(3):277–330,
2005.
Paper presented at NLP4RE’20
[AFGS] Monica Arrabito, Alessandro Fantechi, Stefania Gnesi, and Laura Semini. A comparison of NLP Tools
for RE to extract Variation Points.
[BBDOF] Manlio Bacco, Gianluca Brunori, Felice Dell’Orletta, and Alessio Ferrari. Using NLP to Support
Terminology Extraction and Domain Scoping: Report on the H2020 DESIRA Project.
[dB] Bert de Brock. On System Sequence Descriptions.
[JJT] Vaibhav Jain, Sanskar Jain, and Nishant Tanwar. Cross-Domain Ambiguity Detection using Linear Trans-
formation of Word Embedding Spaces.
[KC] Olivia Kenney and Matt Cooper. QVscribe for Practical and Effective NLP4RE.
[MOU+] Kenji Mori, Naoko Okubo, Yasushi Ueda, Masafumi Katahira, and Toshiyuki Amagasa. Toward
Latent Knowledge Extraction Based on the Correlation of Heterogeneous Text Data Related to Space System
Development.
[WNT] Long Wang, Hiroyuki Nakagawa, and Tatsuhiro Tsuchiya. Opinion Analysis and Organization of Mobile
Application User Reviews.