  Search-based test case generation, which is the application of meta-heuristic
search for generating test cases, has been studied a lot in the literature,
lately. Since, in theory, the performance of meta-heuristic search methods is
highly dependent on their hyper-parameters, there is a need to study
hyper-parameter tuning in this domain. In this paper, we propose a new metric
("Tuning Gain"), which estimates how cost-effective tuning a particular class
is. We then predict "Tuning Gain" using static features of source code classes.
Finally, we prioritize classes for tuning, based on the estimated "Tuning
Gains" and spend the tuning budget only on the highly-ranked classes. To
evaluate our approach, we exhaustively analyze 1,200 hyper-parameter
configurations of a well-known search-based test generation tool (EvoSuite) for
250 classes of 19 projects from benchmarks such as SF110 and SBST2018 tool
competition. We used a tuning approach called Meta-GA and compared the tuning
results with and without the proposed class prioritization. The results show
that for a low tuning budget, prioritizing classes outperforms the alternatives
in terms of extra covered branches (10 times more than a traditional global
tuning). In addition, we report the impact of different features of our
approach such as search space size, tuning budgets, tuning algorithms, and the
number of classes to tune, on the final results.
