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


From: 2022-04-01 14:15 to 15:00
Place: E:1147, LTH E-huset, Ole Römers väg 3, Lund
Contact: per [dot] runeson [at] cs [dot] lth [dot] se
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Dr. Gregory Gay, Chalmers, gives a seminar on his recent work.

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


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 used these algorithms to dynamically set the fitness functions used during generation for the 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 three goals, and show limited improvements on the third when the number of generations of evolution is fixed. Additionally, for two of the three goals, EvoSuiteFIT detects faults missed by the other techniques. The ability to adjust fitness functions allows strategic choices that efficiently produce more effective test suites, and examining these choices offers insight into how to attain our testing goals. We find that adaptive fitness function selection is a powerful technique to apply when an effective fitness function does not already exist for achieving a testing goal.

About the speaker

Gregory Gay is an Associate Professor in the Software Engineering division at Chalmers and the University of Gothenburg. His research interests include automated testing and analysis and search-based software engineering. Our society depends on complex software. Dr. Gay's research blends optimization, artificial intelligence, and machine learning techniques in order to help developers deliver those systems in a safe, secure, and efficient manner. 

Dr. Gay received his Ph.D. from the University of Minnesota, working with the Critical Systems research group, and an M.S. from West Virginia University. He was previously an Assistant Professor at the University of South Carolina.

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