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Requirements Engineering 2021
Mon 20 - Fri 24 September 2021
Thu 23 Sep 2021 08:30 - 09:00 at Hesburgh Library - Machine Learning Chair(s): Zhi Jin

On social media platforms like Twitter, users regularly share their opinions and comments with software vendors and service providers. Popular software products might get thousands of user comments per day. Research has shown that such comments contain valuable information for stakeholders, such as feature ideas, problem reports, or support inquiries. However, it is hard to manually manage and grasp a large amount of user comments, which can be redundant and of a different quality. Consequently, researchers suggested automated approaches to extract valuable comments, e.g., through problem report classifiers. However, these approaches do not aggregate semantically similar comments into specific aspects to provide insights like how often users reported a certain problem. We introduce an approach for automatically discovering topics composed of semantically similar user comments based on deep bidirectional natural language processing algorithms. Stakeholders can use our approach without the need to configure critical parameters like the number of clusters. We present our approach and report on a rigorous multiple-step empirical evaluation to assess how cohesive and meaningful the resulting clusters are. Each evaluation step was peer-coded and resulted in inter-coder agreements of up to 98%, giving us high confidence in the approach. We also report a thematic analysis on the topics discovered from tweets in the telecommunication domain.

Thu 23 Sep

Displayed time zone: Eastern Time (US & Canada) change

08:00 - 09:20
Machine LearningResearch Papers / RE@Next! Papers at Hesburgh Library
Chair(s): Zhi Jin Peking University

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Classifying User Requirements from Online Feedback in Small Dataset Environments using Deep LearningAvailableResearch Paper
Research Papers
Rohan Reddy Mekala Fraunhofer USA CESE, Asif Irfan Fraunhofer USA Center Mid-Atlantic, Eduard C. Groen Fraunhofer IESE, Adam Porter Fraunhofer USA CESE, Mikael Lindvall Fraunhofer USA CESE
Media Attached
Unsupervised Topic Discovery in User CommentsResearch Paper
Research Papers
Christoph Stanik University of Hamburg, Germany, Tim Pietz Universität Hamburg, Walid Maalej University of Hamburg
MARE: an Active Learning Approach for Requirements ClassificationRE@Next
RE@Next! Papers
Cláudia Magalhães Universidade NOVA de Lisboa, João Araújo NOVA LINCS, Universidade NOVA de Lisboa, Alberto Sardinha Instituto Superior Técnico, U. Lisboa & INESC-ID