Natural language processing (NLP) plays an important role in several areas of software engineering, and requirements engineering (RE) is not an exception. Requirements are generally authored and communicated in textual form and in di erent 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 scienti c 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 algorithms has introduced signi cant improvements in the accuracy of various NLP tasks like parsing and POStagging|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 eld 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 Requirements 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 rst two successful editions [DFFP18a, DFF+19] (see summary of the rst edition at [DFFP18b]), the goal of NLP4RE'20 is to strengthen its role as a meeting point for the researchers in the eld, 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 Arti cial 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).
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 Baumer, Paderborn University, Germany Dan Berry, University of Waterloo, Canada Andrea Cimino, CNR-ISTI, Italy Nicolas Sannier, independent researcher [ASBZ16] [DFF+19]
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.
Fabiano Dalpiaz, Alessio Ferrari, Xavier Franch, Sarah Gregory, Frank Houdek, and Cristina Palomares. 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 processing 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 incompleteness in requirements engineering via information visualization and nlp. In International Working Conference on Requirements Engineering: Foundation for Software Quality, pages 119{135. Springer, 2018.
Vincenzo Gervasi and Didar Zowghi. Reasoning about inconsistencies in natural language requirements. ACM Transactions on Software Engineering and Methodology (TOSEM), 14(3):277{330, 2005.
[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 Transformation of Word Embedding Spaces. [KC] Olivia Kenney and Matt Cooper. QVscribe for Practical and E ective 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.