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Search-based test generation is guided by feedback from one or more fitnessfunctions - scoring functions that judge solution optimality. Choosinginformative fitness functions is crucial to meeting the goals of a tester.Unfortunately, many goals - such as forcing the class-under-test to throwexceptions, increasing test suite diversity, and attaining Strong MutationCoverage - do not have effective fitness function formulations. We propose thatmeeting such goals requires treating fitness function identification as asecondary optimization step. An adaptive algorithm that can vary the selectionof fitness functions could adjust its selection throughout the generationprocess to maximize goal attainment, based on the current population of testsuites. To test this hypothesis, we have implemented two reinforcement learningalgorithms in the EvoSuite unit test generation framework, and used thesealgorithms to dynamically set the fitness functions used during generation forthe three goals identified above.  We have evaluated our framework, EvoSuiteFIT, on a set of Java case examples.EvoSuiteFIT techniques attain significant improvements for two of the threegoals, and show limited improvements on the third when the number ofgenerations of evolution is fixed. Additionally, for two of the three goals,EvoSuiteFIT detects faults missed by the other techniques. The ability toadjust fitness functions allows strategic choices that efficiently produce moreeffective test suites, and examining these choices offers insight into how toattain our testing goals. We find that adaptive fitness function selection is apowerful technique to apply when an effective fitness function does not alreadyexist for achieving a testing goal.

Learning How to Search: Generating Effective Test Cases Through Adaptive Fitness Function Selection