Model-Guided Fuzzing of Distributed Systems

We present a coverage-guided testing algorithm for distributed systems implementations. Our main innovation is the use of an abstract formal model of the system that is used to define coverage. Such abstract models are frequently developed in the early phases of protocol design and verification but are infrequently used at testing time. We show that guiding random test generation using model coverage can be effective in covering interesting points in the implementation state space. We have implemented a fuzzer for distributed system implementations and abstract models written in TLA+. Our algorithm achieves better coverage over purely random exploration as well as random exploration guided by different notions of scheduler coverage and mutation. In particular, we show consistently higher coverage on implementations of distributed consensus protocols such as Two-Phase Commit and the Raft implementations in Etcd-raft and RedisRaft and detect bugs faster. Moreover, we discovered 12 previously unknown bugs in their implementations, four of which could only be detected by model-guided fuzzing.