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Full-Text Articles in Engineering
Emerging App Issue Identification From User Feedback: Experience On Wechat, Cuiyun Gao, Wujie Zheng, Yuetang Deng, David Lo, Jichuan Zeng, Michael R. Lyu, Irwin King
Emerging App Issue Identification From User Feedback: Experience On Wechat, Cuiyun Gao, Wujie Zheng, Yuetang Deng, David Lo, Jichuan Zeng, Michael R. Lyu, Irwin King
Research Collection School Of Computing and Information Systems
It is vital for popular mobile apps with large numbers of users to release updates with rich features while keeping stable user experience. Timely and accurately locating emerging app issues can greatly help developers to maintain and update apps. User feedback (i.e., user reviews) is a crucial channel between app developers and users, delivering a stream of information about bugs and features that concern users. Methods to identify emerging issues based on user feedback have been proposed in the literature, however, their applicability in industry has not been explored. We apply the recent method IDEA to WeChat, a popular messenger …
Forecasting Anomalous Events And Performance Correlation Analysis In Event Data, Sonya Leech [Thesis]
Forecasting Anomalous Events And Performance Correlation Analysis In Event Data, Sonya Leech [Thesis]
Dissertations
Classical and Deep Learning methods are quite common approaches for anomaly detection. Extensive research has been conducted on single point anomalies. Collective anomalies that occur over a set of two or more durations are less likely to happen by chance than that of a single point anomaly. Being able to observe and predict these anomalous events may reduce the risk of a server’s performance. This paper presents a comparative analysis into time-series forecasting of collective anomalous events using two procedures. One is a classical SARIMA model and the other is a deep learning Long-Short Term Memory (LSTM) model. It then …