ConfSeer: Leveraging Customer Support Knowledge Bases for Automated Misconfiguration Detection
Summary: ConfSeer mines natural-language customer-support KBs to detect configuration deviations and recommend fixes, addressing high-dimensional parameter matching via NLP, IR, and interactive learning. Deployed on tens of thousands of servers, it achieves 80–97.5% accuracy with low overhead. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Rahul Potharaju (Microsoft)
- 2. Joseph Chan (Microsoft)
- 3. Luhui Hu (Microsoft)
- 4. Cristina Nita-Rotaru (Purdue University)
- 5. Mingshi Wang (Microsoft)
- 6. Liyuan Zhang (Microsoft)
- 7. Navendu Jain (Microsoft)
BibTeX Citation
@article{potharaju_vldb15,
title = {{ConfSeer: Leveraging Customer Support Knowledge Bases for Automated Misconfiguration Detection}},
author = {Potharaju, Rahul and Chan, Joseph and Hu, Luhui and Nita-Rotaru, Cristina and Wang, Mingshi and Zhang, Liyuan and Jain, Navendu},
journal = {PVLDB},
series = {{VLDB} '15},
volume = {8},
number = {12},
pages = {1828--1839},
doi = {10.14778/2824032.2824036},
url = {https://doi.org/10.14778/2824032.2824036},
year = {2015}
}
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