Random testing has proven to be an effective way to catch bugs in distributed systems. This is surprising, as the space of executions is enormous. We provide a theoretical justification of the effectiveness of random testing under various “small depth” hypotheses. 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 which capture the “small depth” hypothesis and are empirically 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.