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In this paper, we present NSGA-II-SVM (Non-dominated Sorting GeneticAlgorithm with Support Vector Machine Guidance), a novel learnable evolutionaryand search-based testing algorithm that leverages Support Vector Machine (SVM)classification models to direct the search towards failure-revealing testinputs. Supported by genetic search, NSGA-II-SVM creates iteratively SVM-basedmodels of the test input space, learning which regions in the search space arepromising to be explored. A subsequent sampling and repetition of evolutionarysearch iterations allow to refine and make the model more accurate in theprediction. Our preliminary evaluation of NSGA-II-SVM by testing an AutomatedValet Parking system shows that NSGA-II-SVM is more effective in identifyingmore critical test cases than a state of the art learnable evolutionary testingtechnique as well as naive random search.

Guiding the Search Towards Failure-Inducing Test Inputs Using Support Vector Machines