一句话：提出 DeepCT，用注意力掩码 GRU 从讨论中增量学习，实现更准更高效的大规模在线服务事件持续分诊。
在线服务系统的事件分诊需将新事件指派给负责团队，对快速恢复至关重要。工业实践表明分诊并非一次完成，而是多团队持续讨论并不断修正的连续过程，事件重分配比例可达 5.43%–68.26%。本文提出 DeepCT：一种基于深度学习的自动持续分诊方法，采用带注意力掩码策略的 GRU 模型和改进损失函数，从讨论中增量学习并更新分诊结果，从而以更少讨论达到正确指派。作者在 Microsoft 14 个大规模在线服务系统上评估，讨论条数从 1 增至 5 时精确识别负责团队的平均准确率为 0.641–0.729，并显著优于当前最佳 bug 分诊方法。
In recent years, online service systems have become increasingly popular. Incidents of these systems could cause significant economic loss and customer dissatisfaction. Incident triage, which is the process of assigning a new incident to the responsible team, is vitally important for quick recovery of the affected service. Our industry experience shows that in practice, incident triage is not conducted only once in the beginning, but is a continuous process, in which engineers from different teams have to discuss intensively among themselves about an incident, and continuously refine the incident-triage result until the correct assignment is reached. In particular, our empirical study on 8 real online service systems shows that the percentage of incidents that were reassigned ranges from 5.43% to 68.26% and the number of discussion items before achieving the correct assignment is up to 11.32 on average. To improve the existing incident triage process, in this paper, we propose DeepCT, a Deep learning based approach to automated Continuous incident Triage. DeepCT incorporates a novel GRU-based (Gated Recurrent Unit) model with an attention-based mask strategy and a revised loss function, which can incrementally learn knowledge from discussions and update incident-triage results. Using DeepCT, the correct incident assignment can be achieved with fewer discussions. We conducted an extensive evaluation of DeepCT on 14 large-scale online service systems in Microsoft. The results show that DeepCT is able to achieve more accurate and efficient incident triage, e.g., the average accuracy identifying the responsible team precisely is 0.641~0.729 with the number of discussion items increasing from 1 to 5. Also, DeepCT statistically significantly outperforms the state-of-the-art bug triage approach.
DOI 原文 ·
@inproceedings{paperbot5330,
title = {Continuous Incident Triage for Large-Scale Online Service Systems},
author = {Junjie Chen and Xiaoting He and Qingwei Lin and Hongyu Zhang and Dan Hao and Feng Gao and Zhangwei Xu and Yingnong Dang and Dongmei Zhang},
booktitle = {ASE},
year = {2019},
doi = {10.1109/ase.2019.00042}
}