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

Bet and Run for Test Case Generation