Learning How to Search: Generating Effective Test Cases Through Adaptive Fitness Function Selection
Hussein Almulla, Gregory Gay

TL;DR
This paper introduces EvoSuiteFIT, an adaptive framework that dynamically selects fitness functions during test generation, significantly improving goal attainment and fault detection in Java test cases through reinforcement learning.
Contribution
It presents a novel adaptive fitness function selection method using reinforcement learning, enhancing search-based test generation effectiveness.
Findings
EvoSuiteFIT improves goal achievement in two out of three test generation goals.
EvoSuiteFIT detects faults missed by traditional methods.
Adaptive fitness function selection is effective when explicit functions are lacking.
Abstract
Search-based test generation is guided by feedback from one or more fitness functions - scoring functions that judge solution optimality. Choosing informative fitness functions is crucial to meeting the goals of a tester. Unfortunately, many goals - such as forcing the class-under-test to throw exceptions, increasing test suite diversity, and attaining Strong Mutation Coverage - do not have effective fitness function formulations. We propose that meeting such goals requires treating fitness function identification as a secondary optimization step. An adaptive algorithm that can vary the selection of fitness functions could adjust its selection throughout the generation process to maximize goal attainment, based on the current population of test suites. To test this hypothesis, we have implemented two reinforcement learning algorithms in the EvoSuite unit test generation framework, and…
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Taxonomy
TopicsSoftware Testing and Debugging Techniques · Software Engineering Research · Viral Infectious Diseases and Gene Expression in Insects
