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

An overwhelming number of users access app repositories like App Store/Google Play and social media platforms like Twitter, where they provide feedback on digital experiences. This vast textual corpus comprising user feedback has the potential to unearth detailed insights regarding the users’ opinions on products and services. Various tools have been proposed that employ natural language processing (NLP) and traditional machine learning (ML) based models as an inexpensive mechanism to identify requirements in user feedback. However, they fall short on their classification accuracy over unseen data due to factors like the cost of generating voluminous de-biased labeled datasets and general inefficiency. Recently, Van Vliet et al. achieved state-of-the-art results extracting and classifying requirements from user reviews through traditional crowdsourcing. Based on their reference classification tasks and outcomes, we successfully developed and validated a deep-learning-backed artificial intelligence pipeline to achieve a state-of-the-art averaged classification accuracy of ~87% on standard tasks for user feedback analysis. This approach, which comprises a BERT-based sequence classifier, proved effective even in extremely low-volume dataset environments. Additionally, our approach drastically reduces the time and costs of evaluation, and improves on the accuracy measures achieved using traditional ML-/NLP-based techniques.

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