中文 English
Home
TECHNICAL PAPERS

Coverage-Directed Test Selection Method for Automatic Test Biasing During Simulation-Based Verification

popularity

New research paper titled “Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification” from researchers at University of Bristol and Infineon Technologies.

Abstract:
“Constrained random test generation is one the most widely adopted methods for generating stimuli for simulation-based verification. Randomness leads to test diversity, but tests tend to repeatedly exercise the same design logic. Constraints are written (typically manually) to bias random tests towards interesting, hard-to-reach, and yet-untested logic. However, as verification progresses, most constrained random tests yield little to no effect on functional coverage. If stimuli generation consumes significantly less resources than simulation, then a better approach involves randomly generating a large number tests, selecting the most effective subset, and only simulating that subset. In this paper, we introduce a novel method for automatic constraint extraction and test selection. This method, which we call coverage-directed test selection, is based on supervised learning from coverage feedback. Our method biases selection towards tests that have a high probability of increasing functional coverage, and prioritises them for simulation. We show how coverage-directed test selection can reduce manual constraint writing, prioritise effective tests, reduce verification resource consumption, and accelerate coverage closure on a large, real-life industrial hardware design.”

Find the technical paper here. Published May 2022.

Masamba, Nyasha, Kerstin Eder, and Tim Blackmore. “Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification.” arXiv preprint arXiv:2205.08524 (2022).

Further Reading:
Semiconductor Engineering Systems & Design channel
AI-Powered Verification
AI can be used in several ways to help existing verification processes, but the biggest gain may come from rethinking some fundamentals.
The Challenges Of Incremental Verification
Is it possible to make a design change and not have to rerun the entire regression suite?



Leave a Reply


(Note: This name will be displayed publicly)