Using augmented BDI models for intelligent stimuli generation

使用增强 BDI 模型进行智能刺激生成

• 批准号: 1953873
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1953873
• 关键词: Using; augmented; BDI; models; intelligent

Constrained random verification (CRV) and formal verification are currently the state-of-the-art approaches to functional verification. CRV's advantage is that does not suffer from size restrictions. Hence it can be applied to large designs. However, CRV is inefficient, with many simulation cycles spent exploring the same state space. Furthermore, guiding the tools into interesting corner cases typically requires considerable manual effort from verification engineers. Conversely, formal verification can find corner cases with little manual steering; but due to complexity limits, it can only be applied exhaustively to relatively small designs. This research aims to employ BDI (Belief-Desire-Intention) intelligence, augmented by machine learning techniques, to investigate the middle ground between CRV and formal verification. A BDI-based approach offers high-level, goal-directed planning with backtracking to achieve set goals. Augmenting a BDI model with a reward/punishment system can facilitate intelligent, agent-based generation of test stimuli which can automatically find and hit the interesting corner cases on a complex design, such as a CPU. This results in less reliance on hand-written constraints and random test generation. The solution would also be applicable to other verification use-cases such as post-silicon debug and test case automation at cluster level.

约束随机验证（CRV）和形式验证是目前最先进的功能验证方法。CRV的优势在于它不受大小限制。因此，它可以应用于大型设计。然而，CRV是低效的，许多模拟周期花费在探索相同的状态空间上。此外，将工具引导到有趣的角落案例通常需要验证工程师进行大量的手工工作。相反，正式的验证可以在很少的手动操作下找到极端情况；但由于复杂性的限制，它只能完全应用于相对较小的设计。本研究旨在利用BDI（信念-欲望-意图）智能，通过机器学习技术的增强，来研究CRV和形式验证之间的中间地带。基于bdi的方法提供高层次的、目标导向的计划，通过回溯来实现设定的目标。用奖励/惩罚系统来增强BDI模型可以促进基于智能体的测试刺激生成，从而可以自动找到并命中复杂设计（如CPU）上有趣的角落案例。这减少了对手写约束和随机测试生成的依赖。该解决方案也将适用于其他验证用例，例如在集群级别上的硅后调试和测试用例自动化。

项目成果

Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification

基于模拟的验证中针对覆盖范围的测试选择的监督学习

• DOI: 10.1109/aitest55621.2022.00012
• 发表时间: 2022
• 作者: Masamba N

Hybrid Intelligent Testing in Simulation-Based Verification

• DOI: 10.1109/aitest55621.2022.00013
• 发表时间: 2022-05
• 期刊: 2022 IEEE International Conference On Artificial Intelligence Testing (AITest)
• 作者: Nyasha Masamba;K. Eder;T. Blackmore