Using Neural Networks for Novelty-based Test Selection to Accelerate Functional Coverage Closure
Software Engineering
2023-06-16 v3 Machine Learning
Abstract
Novel test selectors used in simulation-based verification have been shown to significantly accelerate coverage closure regardless of the number of coverage holes. This paper presents a configurable and highly-automated framework for novel test selection based on neural networks. Three configurations of this framework are tested with a commercial signal processing unit. All three convincingly outperform random test selection with the largest saving of simulation being 49.37% to reach 99.5% coverage. The computational expense of the configurations is negligible compared to the simulation reduction. We compare the experimental results and discuss important characteristics related to the performance of the configurations.
Cite
@article{arxiv.2207.00445,
title = {Using Neural Networks for Novelty-based Test Selection to Accelerate Functional Coverage Closure},
author = {Xuan Zheng and Kerstin Eder and Tim Blackmore},
journal= {arXiv preprint arXiv:2207.00445},
year = {2023}
}