一句话：提出 iFeedback，用用户反馈文本中的词组合指标和机器学习实现大规模在线系统的实时异常检测与摘要。
大规模在线系统复杂且快速演化，尽管经过测试仍难以避免缺陷。后端监控无法发现 UI 相关缺陷、对后端指标影响较小的问题或第三方协作系统错误等。用户反馈能覆盖这些类型的问题。本文介绍 iFeedback 的设计：它从用户反馈文本中抽取基于词组合的指标，并用机器学习算法进行快速系统异常检测，从而充当系统生命状态监控器；随后对文本进行摘要，以便开发者有效理解异常并做根因分析。作者在数十个大规模生产在线服务系统中部署 iFeedback 十个月，报告了具有代表性的成功经验。
Large-scale online systems are complex, fast-evolving, and hardly bug-free despite the testing efforts. Backend system monitoring cannot detect many types of issues, such as UI related bugs, bugs with small impact on backend system indicators, or errors from third-party co-operating systems, etc. However, users are good informers of such issues: They will provide their feedback for any types of issues. This experience paper discusses our design of iFeedback, a tool to perform real-time issue detection based on user feedback texts. Unlike traditional approaches that analyze user feedback with computation-intensive natural language processing algorithms, iFeedback is focusing on fast issue detection, which can serve as a system life-condition monitor. In particular, iFeedback extracts word combination-based indicators from feedback texts. This allows iFeedback to perform fast system anomaly detection with sophisticated machine learning algorithms. iFeedback then further summarizes the texts with an aim to effectively present the anomaly to the developers for root cause analysis. We present our representative experiences in successfully applying iFeedback in tens of large-scale production online service systems in ten months.
DOI 原文 ·
@inproceedings{paperbot5235,
title = {iFeedback: Exploiting User Feedback for Real-Time Issue Detection in Large-Scale Online Service Systems},
author = {Wujie Zheng and Haochuan Lu and Yangfan Zhou and Jianming Liang and Haibing Zheng and Yuetang Deng},
booktitle = {ASE},
year = {2019},
doi = {10.1109/ase.2019.00041}
}