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<!doctype html>
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<title>NLP4RE'22</title>
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<!-- <h3 class="header-sub-title wow fadeInUp" data-wow-duration="1.3s" data-wow-delay="0.2s">Submit with us ...</h3> -->
<h2 class="header-title wow fadeInUp" data-wow-duration="1.3s" data-wow-delay="0.5s">5th Workshop on Natural Language Processing for Requirements Engineering</h2>
<p class="text wow fadeInUp" data-wow-duration="1.3s" data-wow-delay="0.8s">REFSQ'22 Workshop, March 21st, 2022</p>
<a href="https://easychair.org/conferences/?conf=nlp4re22" class="main-btn wow fadeInUp" data-wow-duration="1.3s" data-wow-delay="1.1s">Submit Here!</a>
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<h4 class="services-title mb-20">Important Dates</h4>
<ul style="list-style-type: none;">
<li>Paper Submission: <span class="font-weight-bold">January 23rd, 2022 (extended)</span></li>
<li>Author notification: <span class="font-weight-bold">February 17th, 2022</span></li>
<li>Camera Ready: <span class="font-weight-bold">February 28th, 2022</span></li>
<li>Workshop: <span class="font-weight-bold">March 21st, 2022</span></li>
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<p class="free-text">Natural language processing (NLP) has played an important role in several areas of computer science, and requirements engineering (RE) is not an exception. In the last years, the advent of massive and very heterogeneous natural language (NL) RE-relevant sources, like tweets and app reviews, has attracted even more interest from the RE community.</p>
<p class="free-text">The main goal of the NLP4RE workshop is to serve as a regular meeting point for the researchers on NLP technologies in RE. NLP4RE aims to promote the timely communication of advances, challenges and barriers that the researchers encounter, and the workshop wishes to provide a friendly venue where collaborations may emerge naturally.</p>
<p>The NLP4RE Workshop is co-located with REFSQ'22. Check out the REFSQ'22 Conference here: <a href="https://2022.refsq.org">https://2022.refsq.org</a></p>
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<p class="text">The workshop welcomes contributions regarding both theory and application of NLP technologies in RE. We also encourage contributions that highlight challenges faced by industrial practitioners when dealing with requirements expressed in NL, and the experiences of academics in technology transfer.</p>
<p class="text">We are interested in Tool Papers (see the <a class="page-scroll" href="#call-for-papers">Call for Papers</a>), in which the authors provide a brief description of an NLP tool for RE, and a plan for a tool demo at the workshop.</p>
<p class="text">We are also interested in Report Papers (see the <a class="page-scroll" href="#call-for-papers">Call for Papers</a>), in which the authors provide an overview on the current and past research of their teams. These contributions do not require novelty with respect to previous work, because the main goal of the workshop is to foster discussion and networking.</p>
<p class="text">Within the area of NLP for RE, the topics of interest of the workshop include but are not limited to:</p>
<p class="text">
<ul>
<li>Requirements quality assessment</li>
<li>App Review analysis and classification</li>
<li>Tweet mining and analysis for RE</li>
<li>Bug report mining and analysis for RE</li>
<li>Automated requirements management</li>
<li>Multi-modal requirements analysis</li>
<li>Ambiguity and defect detection in requirements</li>
<li>Requirements tracing</li>
<li>Requirements retrieval</li>
<li>Domain-specific ontology learning</li>
<li>Functional and non-functional requirements categorisation</li>
<li>Model synthesis from requirements</li>
<li>Information extraction (abstraction identification, feature extraction)</li>
<li>Formalisation of informal requirements</li>
<li>Question-answering systems for RE</li>
<li>Discourse analysis for RE</li>
<li>Argumentation for RE</li>
<li>Summarisation of requirements documents</li>
<li>Structure assessment for requirements documents</li>
<li>Completeness assessment for requirements documents</li>
<li>Speech-to-text and speech analysis in requirements elicitation</li>
<li>Requirements datasets</li>
</ul>
</p>
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<h5>Technical Design Papers</h5>
<p class="text"><span style="font-weight: bold;">8 pages (plus 1 page for references)</span>, describing novel technical solutions for the application of NLP technologies to RE-relevant artifacts. The papers in this category include preliminary solutions to RE problems, with an early validation.</p>
<h5>Experience Papers</h5>
<p class="text"><span style="font-weight: bold;">8 pages (plus 1 page for references)</span>, describing practical experiences in the application of NLP technologies to RE-relevant artifacts. The papers in this category include experience reports, industrial case studies, controlled experiments, and other types of empirical studies conducted to practically assess existing technical solutions.</p>
<h5>Report Papers</h5>
<p class="text"><span style="font-weight: bold;">4 pages (plus 1 page for references)</span>, in which the authors provide an overview on the current and past research of their team, describing what they have been doing on the workshop’s topics, and/or what they are doing, and/or what they plan to do. These contributions do not require novelty with respect to previous work, and are oriented to foster discussion and networking. A non-mandatory template for Report Papers can be dowloaded here. Please notice that we do not allow submission of Report Papers from teams who submitted these types of papers to NLP4RE’19 and NLP4RE’20.</p>
<p>Our recommended template for Report Papers can be dowloaded <a href="assets/paper-templates/nlp4re-22-reports-template.zip">here</a>.</p>
<h5>Vision Papers</h5>
<p class="text"><span style="font-weight: bold;">4 pages (plus 1 page for references)</span>, outlining roadmaps for research in the workshop’s topics, including industrial and research challenges based on currently available knowledge. Specifically, we encourage contributions that highlight challenges faced by industrial practitioners when dealing with requirements expressed in NL, and faced by academics in technology transfer studies.</p>
<h5>Tool Papers</h5>
<p class="text"><span style="font-weight: bold;">4 pages (including screenshots and references)</span>, in which the authors provide a short description of an NLP-based tool for RE with screenshots, together with a clear plan for a demo at the workshop. These contributions do not require novelty with respect to previous work, and the authors can also showcase tools that have been presented in past conferences and workshops. These papers will be evaluated based on the potential interest raised by the tool, and based on the clarity of the plan for the demo.</p>
<p class="text">
Submissions should be written in English and submitted in PDF format (page size A4, single column) formatted according to the CEUR Proceedings Style:
<ul>
<li>Overleaf CEURArt Template (please use 1-column style): <a href="https://www.overleaf.com/read/gwhxnqcghhdt">https://www.overleaf.com/read/gwhxnqcghhdt</a></li>
<li>CEUR Template Download: <a href="http://ceur-ws.org/Vol-XXX/CEURART.zip">http://ceur-ws.org/Vol-XXX/CEURART.zip</a></li>
</ul>
</p>
<p class="text">All papers will be peer-reviewed by three members of the Program Committee, and will appear in the CEUR Workshop Proceedings, with ISBN number.</p>
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</section>
<!--====== KEYNOTE ======-->
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<h3 class="title">Keynote: <span>Dr. Nicole Novielli</span></h3>
<h6>On Designing SE-specific Sentiment Analysis Tools</h6>
</div>
<p class="text">Do you like your code? What kind of code makes developers happiest? What makes them angriest? Is it possible to monitor the mood of a large team of coders to determine when and where a codebase needs additional help? How do emotions act as a proxy for developers’ wellbeing and productivity? Answering these questions involves being able to design and implement reliable tools for sentiment analysis, which is the automatic processing of texts to map and capture the polarity of emotions and opinions. In this talk, I will provide an overview of recent research about sentiment analysis in software engineering (SE), address the open challenges, and provide empirically-based guidelines for safe (re)use of SE-specific tools in order to obtain meaningful results.</p>
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<img src="assets/images/keynote-profile.jpeg" alt="about" class="mb-50" style="width: 60%">
<p class="text" style="text-align: justify;">Nicole Novielli is an Assistant Professor at the University of Bari. Her research interests lie at the intersection of software engineering and affective computing with a specific focus on emotion mining from software repositories, natural language processing of developers’ communication traces, and biometric recognition of developers’ emotions. You can find more about Nicole at <a href="https://collab.di.uniba.it/nicole/">her website</a>.</p>
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<!--====== PROGRAM ======-->
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<h3 class="title">Program:<span> Monday, March 21st</span></h3>
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Session 1 <span class="ml-2 font-weight-light">(9:00 - 10:30)</span>
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<span class="text-inverse op-8">9:00 - 9:10</span> - Joint Introduction to NLP4RE and RE4AI
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<span class="text-inverse op-8">9:10 - 9:40</span> - <strong>Ethical User stories: Industrial study</strong> by Erika Halme, Agbese Mamia, Jani Antikainen, Hanna-Kaisa Alanen, Marianna Jantunen, Arif Ali Khan, Kai-Kristian Kemell, Ville Vakkuri and Pekka Abrahamsson. [from RE4AI]
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<span class="text-inverse op-8">9:40 - 10:00</span> - <strong>PReDUS: A Privacy Requirements Detector from User Stories</strong> by Francesco Casillo, Vincenzo Deufemia and Carmine Gravino
</p>
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<span class="text-inverse op-8">10:00 - 10:30</span> - <strong>How NLG Can Support Validation in RE</strong> by Bert de Brock and Coen Suurmond
</p>
</li>
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<span class="text-inverse op-8">11:00 - 11:30</span> - <strong>Automated Requirement Formalization using Product Design Specifications</strong> by Robin Gröpler, Libin Kutty, Viju Sudhi and Daran Smalley
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<span class="text-inverse op-8">11:30 - 12:00</span> - <strong>Using NLP tools to detect ambiguities in system requirements - A comparison study</strong> by Aleksandar Bajceta, Miguel Leon Ortiz, Wasif Afzal, Pernilla Lindberg and Markus Bohlin
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<h5 class="holder-name"><a href="#">Lloyd Montgomery</a></h5>
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<li>Sallam Abualhaija, University of Luxembourg, Luxembourg </li>
<li>Chetan Arora, Deakin University, Australia</li>
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<li>Henning Femmer, Fachhochschule Südwestfalen, Germany</li>
<li>Alessio Ferrari, CNR-ISTI, Italy</li>
<li>Xavier Franch, Universitat Politècnica de Catalunya, Spain</li>
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<li>Davide Fucci, Blekinge Institute of Technology, Sweden</li>
<li>Sepideh Ghanavati, University of Maine, USA</li>
<li>Jin Guo, McGill University, Canada</li>
<li>Emitzá Guzmán, Vrije Universiteit Amsterdam, The Netherlands</li>
<li>Eduard Groen, Fraunhofer IESE, Germany</li>
<li>Frank Houdek, Daimler Ag, Germany</li>
<li>Clara Lüders, University of Hamburg, Germany</li>
<li>Luisa Mich, University of Trento, Italy</li>
<li>Nan Niu, University of Cincinnati, USA</li>
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