Configuration errors remain a major cause of system failures and service outages. One promising approach to identify configuration errors automatically is to learn common usage patterns (and anti-patterns) using data-driven methods. However, existing data-driven learning approaches analyze only simple configurations ( e.g. , those with no hierarchical structure), identify only simple types of issues ( e.g. , type errors), or require extensive domain-specific tuning. In this paper, we present D iffy , the first push-button configuration analyzer that detects likely bugs in structured configurations. From example configurations, D iffy learns a common template, with "holes" that capture their variation. It then applies unsupervised learning to identify anomalous template parameters as likely bugs. We evaluate D iffy on a large cloud provider’s wide-area network, an operational 5G network testbed, and MySQL configurations, demonstrating its versatility, performance, and accuracy. During D iffy ’s development, it caught and prevented a bug in a configuration timer value that had previously caused an outage for the cloud provider.

@article{KakarlaYB24,
  title = {Diffy: Data-Driven Bug Finding for Configurations},
  author = {Siva Kesava Reddy Kakarla and Francis Y. Yan and Ryan Beckett},
  journal = {Proceedings of the ACM on Programming Languages},
  volume = {8},
  number = {PLDI},
  year = {2024},
  doi = {10.1145/3656385}
}