  For simulation-based systems, finding a set of test cases with the least cost
by exploring multiple goals is a complex task. Domain-specific optimization
goals (e.g. maximize output variance) are useful for guiding the rapid
selection of test cases via mutation. But evaluating the selected test cases
via mutation (that can distinguish the current program from the mutated
systems) is a different goal to domain-specific optimizations. While the
optimization goals can be used to guide the mutation analysis, that guidance
should be viewed as a weak indicator since it can hurt the mutation
effectiveness goals by focusing too much on the optimization goals. Based on
the above, this paper proposes DoLesS (Domination with Least Squares
Approximation) that selects the minimal and effective test cases by averaging
over a coarse-grained grid of the information gained from multiple
optimizations goals. DoLesS applies an inverted least squares approximation
approach to find a minimal set of tests that can distinguish better from worse
parts of the optimization goals. When tested on multiple simulation-based
systems, DoLesS performs as well or even better as the prior state-of-the-art,
while running 80-360 times faster on average (seconds instead of hours).
