  Anyone working in the technology sector is probably familiar with the
question: "Have you tried turning it off and on again?", as this is usually the
default question asked by tech support. Similarly, it is known in search based
testing that metaheuristics might get trapped in a plateau during a search. As
a human, one can look at the gradient of the fitness curve and decide to
restart the search, so as to hopefully improve the results of the optimization
with the next run. Trying to automate such a restart, it has to be
programmatically decided whether the metaheuristic has encountered a plateau
yet, which is an inherently difficult problem. To mitigate this problem in the
context of theoretical search problems, the Bet and Run strategy was developed,
where multiple algorithm instances are started concurrently, and after some
time all but the single most promising instance in terms of fitness values are
killed. In this paper, we adopt and evaluate the Bet and Run strategy for the
problem of test case generation. Our work indicates that use of this restart
strategy does not generally lead to gains in the quality metrics, when
instantiated with the best parameters found in the literature.
