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Random testing has proven to be an effective way to catch bugs in distributed systems in the presence of network partition faults. This is surprising, as the space of potentially faulty executions is enormous, and the bugs depend on a subtle interplay between sequences of operations and faults.  We provide a theoretical justification of the effectiveness of random testing in this context. First, we show a general construction, using the probabilistic method from combinatorics, that shows that whenever a random test covers a fixed coverage goal with sufficiently high probability, a small randomly-chosen set of tests achieves full coverage with high probability. In particular, we show that our construction can give test sets exponentially smaller than systematic enumeration. Second, based on an empirical study of many bugs found by random testing in production distributed systems, we introduce notions of test coverage relating to network partition faults which are effective in finding bugs. Finally, we show using combinatorial arguments that for these notions of test coverage we introduce, we can find a lower bound on the probability that a random test covers a given goal. Our general construction then explains why random testing tools achieve good coverage---and hence, find bugs---quickly.  While we formulate our results in terms of network partition faults, our construction provides a step towards rigorous analysis of random testing algorithms, and can be applicable in other scenarios.

Why is random testing effective for partition tolerance bugs?