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MSR 2021
Mon 17 - Wed 19 May 2021
co-located with ICSE 2021
Wed 19 May 2021 02:17 - 02:20 at MSR Room 2 - NLP Chair(s): Chunyang Chen

Context: Mobile app reviews written by users on app stores or social media are significant resources for app developers. Analyzing app reviews have proved to be useful for many areas of software engineering (e.g. requirement engineering, testing). Automatic classification of app reviews requires extensive efforts to manually curate a labeled dataset. When the classification purpose changes (e.g. identifying bugs versus usability issues or sentiment), new datasets should be labeled, which prevents the extensibility of the developed models for new desired classes/tasks in practice. Recent pre-trained neural language models (PTM) are trained on large corpora in an unsupervised manner and have found success in solving similar Natural Language Processing problems. However, the applicability of PTMs is not explored for app review classification.

Objective: We investigate the benefits of PTMs for app review classification compared to the existing models, as well as the transferability of PTMs in multiple settings.

Method: We empirically study the accuracy and time efficiency of PTMs compared to prior approaches using six datasets from literature. In addition, we investigate the performance of the PTMs trained on app reviews (i.e. domain-specific PTMs). We set up different studies to evaluate PTMs in multiple settings: binary vs. multi-class classification, zero-shot classification (when new labels are introduced to the model), multi-task setting, and classification of reviews from different resources. The datasets are manually labeled app review datasets from Google Play Store, Apple App Store, and Twitter data. In all cases, Micro and Macro Precision, Recall, and F1-scores will be used and we will report the time required for training and prediction with the models.

Wed 19 May

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

02:00 - 02:50
NLPRegistered Reports / Technical Papers at MSR Room 2
Chair(s): Chunyang Chen Monash University
02:01
4m
Talk
Automatic Part-of-Speech Tagging for Security Vulnerability Descriptions
Technical Papers
SOFONIAS YITAGESU Tianjin University, Xiaowang Zhang Tianjin University, Zhiyong Feng Tianjin University, Li Xiaohong TianJin University, Zhenchang Xing Australian National University
Pre-print
02:05
4m
Talk
Attention-based model for predicting question relatedness on Stack Overflow
Technical Papers
Jiayan Pei South China University of Technology, Yimin Wu South China University of Technology, Research Institute of SCUT in Yangjiang, Zishan Qin South China University of Technology, Yao Cong South China University of Technology, Jingtao Guan Research Institute of SCUT in Yangjiang
Pre-print
02:09
4m
Talk
Characterising the Knowledge about Primitive Variables in Java Code Comments
Technical Papers
Mahfouth Alghamdi The University of Adelaide, Shinpei Hayashi Tokyo Institute of Technology, Takashi Kobayashi Tokyo Institute of Technology, Christoph Treude University of Adelaide
Pre-print
02:13
4m
Talk
Googling for Software Development: What Developers Search For and What They Find
Technical Papers
Pre-print
02:17
3m
Talk
Evaluating Pre-Trained Models for User Feedback Analysis in Software Engineering: A Study on Classification of App-Reviews
Registered Reports
Mohammad Abdul Hadi University of British Columbia, Fatemeh Hendijani Fard University of British Columbia
Pre-print
02:20
3m
Talk
Cross-status Communication and Project Outcomes in OSS Developmentā€“A Language Style Matching Perspective
Registered Reports
Yisi Han Nanjing University, Zhendong Wang University of California, Irvine, Yang Feng State Key Laboratory for Novel Software Technology, Nanjing University, Zhihong Zhao Nanjing Tech Unniversity, Yi Wang Beijing University of Posts and Telecommunications
Pre-print
02:23
27m
Live Q&A
Discussions and Q&A
Technical Papers


Information for Participants
Wed 19 May 2021 02:00 - 02:50 at MSR Room 2 - NLP Chair(s): Chunyang Chen
Info for room MSR Room 2:

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