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.