Research Direction



Prioritizing Issues

Extract the user-concerned issues from app reviews of each version, and report the import ones of current versions to developers.

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Emerging Issue Detection

Based on online topic modeling, we capture the emergent issues of the current versions for facilitate app updating.

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Short Text Understanding

Focusing on understanding short texts, we conduct several on-going related tasks, such as classification.

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Project at a Glance


RRGen [ASE'19]

We propose a novel and automated framework named RRGen, which aims to automating app review response generation.



IDEA [ICSE'18]

We propose a novel and automated framework named IDEA, which aims to IDentify Emerging App issues effectively based on online reviewanalysis.

INFAR [FSE Demo'18]

We propose a framework named INFAR, which aims to automatically summarize insights from user reviews along with time periods and multiple dimensions.

CrossMiner [ISSRE'16]

We propose a novel framework named CrossMiner to analyze the essential app issues and explore whether the app issues exhibit differently on different platforms.

PAID [ISSRE'15]

we target at designing a framework in Prioritizing App Issues for Developers (PAID) with minimal manual power and good accuracy.

AR-Tracker [SOSE'15]

We implement AR-Tracker, a new framework to mine user reviews without the need of human labeling and track the dynamics from the top-ranked reviews.

IntelliAd [ICSE-C'17]

We design a tool named IntelliAd to automatically measure the ads-related consumption on mobile phones.





Publication


  • Cuiyun Gao, Jichuan Zeng, Michael R. Lyu, and Irwin King. Online App Review Analysis for Identifying Emerging Issues. In Proceedings of the 40th International Conference on Software Engineering (ICSE), 2018. get_app code

  • Cuiyun Gao, Yichuan Man, Hui Xu, Jieming Zhu, Yangfan Zhou, and Michael R. Lyu. IntelliAd: Assisting Mobile App Developers in MeasuringAd Costs Automatically. In Proceedings of the 39th International Conference on Software Engineering (ICSE), 2017. get_app code

  • Cuiyun Gao, Hui Xu, Yichuan Man, Yangfan Zhou, and Michael R. Lyu. IntelliAd: Understanding In-APP Ad Costs From Users’ Perspective. In arXiv:1607.03575, 2016. get_app

  • Yichuan Man, Cuiyun Gao, Michael R. Lyu, and Jiuchun Jiang. Experience Report: Understanding Cross-Platform App Issues From User Reviews. In Proceedings of the 27th International Sysposium on Software Reliability Engineering (ISSRE). IEEE, 2016. get_app code

  • Cuiyun Gao, Baoxiang Wang, Pinjia He, Jieming Zhu, Yangfan Zhou, and Michael R. Lyu. PAID: Proritizing App Issues for Developers by Tracking User Reviews Over Versions. In Proceedings of the 26th International Sysposium on Software Reliability Engineering (ISSRE). IEEE, 2015. get_app code

  • Hui Xu, Yangfan Zhou, Cuiyun Gao, Yu Kang, and Michael R. Lyu. SpyAware: Investigating the Privacy Leakage Signatures in App Execution Traces. In Proceedings of the 26th International Sysposium on Software Reliability Engineering (ISSRE). IEEE, 2015. get_app

  • Cuiyun Gao, Hui Xu, Junjie Hu, and Yangfan Zhou. AR-TRacker: Track the Dynamics of Mobile Apps via User Review Mining. In Proceedings of the Symposium on Service-Oriented System Engineering (SOSE). IEEE, 2015. get_app code





Team



140x140 Michael R. Lyu

Professor, CUHK ACM Fellow, IEEE Fellow, AAAS Fellow

140x140 Cuiyun Gao

Research Fellow, Nanyang Technology University

140x140 Jichuan Zeng

Ph.D, CUHK

140x140 Yichuan Man

Ph.D, CUHK

140x140 Zhicong Zhong

B.S. Student, Sun Yat-Sen University