NOVA: Coordinated Test Selection and Bayes-Optimized Constrained Randomization for Accelerated Coverage Closure

2025/11/30 by Weijie Peng, Nanbing Li, Peng, Weijie +13
Computer Science · #Software Testing and Debugging Techniques #Advanced Multi-Objective Optimization Algorithms #Adversarial Robustness in Machine Learning

paper · pdf · doi:10.48550/arxiv.2512.00688

Abstract

Functional verification relies on large simulation-based regressions. Traditional test selection relies on static test features and overlooks actual coverage behavior, wasting substantial simulation time, while constrained random stimuli generation depends on manually crafted distributions that are difficult to design and often ineffective. We present NOVA, a framework that coordinates coverage-aware test selection with Bayes-optimized constrained randomization. NOVA extracts fine-grained coverage features to filter redundant tests and modifies the constraint solver to expose parameterized decision strategies whose settings are tuned via Bayesian optimization to maximize coverage growth. Across multiple RTL designs, NOVA achieves up to a 2.82× coverage convergence speedup without requiring human-crafted heuristics.

Citations

• Towards Comprehensive Sampling of SMT Solutions
• Detecting Stimuli with Novel Temporal Patterns to Accelerate Functional Coverage Closure
• High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces
• A Unified Approach to Interpreting Model Predictions

Related

• Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification
• Using Neural Networks for Novelty-based Test Selection to Accelerate Functional Coverage Closure
• More powerful multiple testing under dependence via randomization
• Detecting Stimuli with Novel Temporal Patterns to Accelerate Functional Coverage Closure
• OptPO: Optimal Rollout Allocation for Test-time Policy Optimization