Smart Issue Detection for Large-Scale Online Service Systems Using Multi-Channel Data

Liushan Chen, Yu Pei, Mingyang Wan, Zhihui Fei, Tao Liang, Guojun Ma

Abstract: Given the scale and complexity of large online service systems and the diversity of environments in which the services are to be invoked, it is inevitable that those service systems contain bugs that affect the users. As a result, it is essential for service providers to discover issues in their systems based on information gathered from users. iFeedback is a state-of-the-art technique for user-feedback-based issue detection. While it has been deployed to help detect issues in real-world service systems, the accuracy of iFeedback’s detection results is relatively low due to limitations in its design. In this paper, we propose the SkyNet technique and tool that analyzes both user feedback gathered via specific channels and public posts collected from social media platforms to more accurately detect issues in service systems. We have applied the tool to detect issues for three real-world, large-scale online service systems based on their historical data gathered over a ten-month period of time. SkyNet reported in total 2790 issues, among which 93.0% were confirmed by developers as reflecting real problems that deserve their close attention. It also detected 58 out of the 62 severe issues reported during the period, achieving a recall of 93.5% for severe issues. Such results suggest SkyNet is both effective and accurate in issue detection.