Guiding the Search Towards Failure-Inducing Test Inputs Using Support Vector Machines
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916103183138816 |
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| author | Sorokin, Lev Kerscher, Niklas |
| author_facet | Sorokin, Lev Kerscher, Niklas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_12364 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Guiding the Search Towards Failure-Inducing Test Inputs Using Support Vector Machines Sorokin, Lev Kerscher, Niklas Software Engineering Neural and Evolutionary Computing 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. |
| title | Guiding the Search Towards Failure-Inducing Test Inputs Using Support Vector Machines |
| topic | Software Engineering Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2401.12364 |