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