Large scale production distributed systems are difficult to design and test.
Correctness must be ensured when processes run asynchronously, at arbitrary rates relative to each other,
and in the presence of failures, e.g., process crashes or message losses. These conditions
create a huge space of executions that is difficult to explore in a principled way.
Current testing techniques focus on systematic or randomized exploration of all executions
of an implementation while treating the implemented algorithms as black boxes.
On the other hand, proofs of correctness of many of the underlying algorithms often exploit
semantic properties that reduce reasoning about correctness to a subset of behaviors.
For example, the \emph{communication-closure} property, used in many proofs of distributed
consensus algorithms, shows that every asynchronous execution of the algorithm is equivalent to
a \emph{lossy synchronous} execution, thus reducing the burden of proof to only that subset.
In a lossy synchronous execution, processes execute in lock-step rounds, and messages are either
received in the same round or lost forever—such executions form a small subset of all asynchronous ones.

We formulate the \emph{communication-closure hypothesis},
which states that bugs in implementations of distributed consensus algorithms will already manifest in
lossy synchronous executions and present a testing algorithm based on this hypothesis.
We prioritize the search space based on a bound on the number of failures in the execution
and the rate at which these failures are recovered.
We show that a random testing algorithm based on sampling lossy synchronous executions
can empirically find a number of bugs—including previously unknown ones—in production
distributed systems such as Zookeeper, Cassandra, and Ratis,
and also produce more understandable bug traces.