Large services depend on correct configuration to run efficiently and seamlessly. Checking such configuration for correctness has become a very important problem because services use a large and continuously increasing number of configuration files and parameters. Yet, very few such tools exist because the definition of correctness for a configuration parameter is seldom specified or documented, existing either as tribal knowledge among a few domain experts or not at all.
In this paper, we address the problem of configuration pattern mining: learning configuration rules from example. Using program synthesis and a novel string profiling algorithm, we show that we can use file contents and histories of commits to learn patterns in configuration. We have built a tool called ConfMiner that implements configuration pattern mining and have deployed it on four large repositories containing configuration for a large-scale enterprise service. Our evaluation shows that ConfMiner learns a large variety of configuration rules with high precision and is very useful in flagging anomalous configuration.
Thu 18 NovDisplayed time zone: Hobart change
18:00 - 19:00
|Learning Patterns in Configuration|
|Transcode: Detecting Status Code Mapping Errors in Large-Scale Systems|
Wensheng Tang The Hong Kong University of Science and Technology, Yikun Hu The Hong Kong University of Science and Technology, Gang Fan Hong Kong University of Science and Technology, Peisen Yao Hong Kong University of Science and Technology; Ant Group, Rongxin Wu Xiamen University, Guangyuan Bai Tencent Inc., Pengcheng Wang Tencent, China, Charles Zhang Hong Kong University of Science and Technology
|Evolutionary-Guided Synthesis of Verified Pareto Optimal Policies|