Preface: 3rd Workshop on Natural Language Processing for Requirements Engineering (NLP4RE 2020)

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 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 algorithms has introduced significant 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 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 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 first two successful editions [DFFP18a, DFF19] (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].