# Using Neural Networks for Novelty-based Test Selection to Accelerate Functional Coverage Closure

arXiv: https://arxiv.org/abs/2207.00445 · HTML: https://mlsanity.com/paper/2207.00445/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 1 Jul 2022.

## Builds on (most cited first, 1 of 1)

- [Distance-based Confidence Score for Neural Network Classifiers](https://mlsanity.com/paper/1709.09844/) (2017)

## Built on by (most cited first, 5 of 5)

- [Efficient Stimuli Generation using Reinforcement Learning in Design Verification](https://mlsanity.com/paper/2405.19815/) (2024)
- [Detecting Stimuli with Novel Temporal Patterns to Accelerate Functional Coverage Closure](https://mlsanity.com/paper/2407.02510/) (2024)
- [Review of Machine Learning for Micro-Electronic Design Verification](https://mlsanity.com/paper/2503.11687/) (2025)
- [Optimizing Coverage-Driven Verification Using Machine Learning and PyUVM: A Novel Approach](https://mlsanity.com/paper/2503.11666/) (2025)
- [Formal-Verification-Guided Bayesian Optimization for Non-Intrusive Last-Mile Coverage Closure](https://mlsanity.com/paper/2610.17449/) (2026)

## Abstract (arXiv)

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.