AutoARTS: Taxonomy, Insights and Tools for Root Cause Labelling of Incidents in Microsoft Azure


Pradeep Dogga, UCLA; Chetan Bansal, Richard Costleigh, Gopinath Jayagopal, Suman Nath, and Xuchao Zhang, Microsoft


Labelling incident postmortems with the root causes is essential for aggregate analysis, which can reveal common problem areas, trends, patterns, and risks that may cause future incidents. A common practice is to manually label postmortems with a single root cause based on an ad hoc taxonomy of root cause tags. However, this manual process is error-prone, a single root cause is inadequate to capture all contributing factors behind an incident, and ad hoc taxonomies do not reflect the diverse categories of root causes.

In this paper, we address this problem with a three-pronged approach. First, we conduct an extensive multi-year analysis of over 2000 incidents from more than 450 services in Microsoft Azure to understand all the factors that contributed to the incidents. Second, based on the empirical study, we propose a novel hierarchical and comprehensive taxonomy of potential contributing factors for production incidents. Lastly, we develop an automated tool that can assist humans in the labelling process. We present empirical evaluation and a user study that show the effectiveness of our approach. To the best of our knowledge, this is the largest and most comprehensive study of production incident postmortem reports yet. We also make our taxonomy publicly available.

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@inproceedings {288815,
author = {Pradeep Dogga and Chetan Bansal and Richard Costleigh and Gopinath Jayagopal and Suman Nath and Xuchao Zhang},
title = {{AutoARTS}: Taxonomy, Insights and Tools for Root Cause Labelling of Incidents in Microsoft Azure},
booktitle = {2023 USENIX Annual Technical Conference (USENIX ATC 23)},
year = {2023},
isbn = {978-1-939133-35-9},
address = {Boston, MA},
pages = {359--372},
url = {},
publisher = {USENIX Association},
month = jul