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

arXiv: https://arxiv.org/abs/2401.12364 · HTML: https://mlsanity.com/paper/2401.12364/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2024.

## Built on by (most cited first, 2 of 2)

- [Can Search-Based Testing with Pareto Optimization Effectively Cover Failure-Revealing Test Inputs?](https://mlsanity.com/paper/2410.11769/) (2024)
- [Simulator Ensembles for Trustworthy Autonomous Driving Systems Testing](https://mlsanity.com/paper/2503.08936/) (2025)

## Abstract (arXiv)

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.