A UCB-based Tree Search Approach to Joint Verification-Correction Strategy for Large Scale Systems

TL;DR

This paper introduces a UCB-based tree search method to optimize joint verification and correction strategies in large-scale systems, addressing the challenge of planning with two different activity types.

Contribution

It presents a novel UCB-based tree search algorithm combined with ensemble learning to effectively plan joint verification-correction strategies for large systems.

Findings

Effective in identifying near-optimal strategies

Handles large-scale system complexity

Outperforms traditional planning methods

Abstract

Verification planning is a sequential decision-making problem that specifies a set of verification activities (VA) and correction activities (CA) at different phases of system development. While VAs are used to identify errors and defects, CAs also play important roles in system verification as they correct the identified errors and defects. However, current planning methods only consider VAs as decision choices. Because VAs and CAs have different activity spaces, planning a joint verification-correction strategy (JVCS) is still challenging, especially for large-size systems. Here we introduce a UCB-based tree search approach to search for near-optimal JVCSs. First, verification planning is simplified as repeatable bandit problems and an upper confidence bound rule for repeatable bandits (UCBRB) is presented with the optimal regret bound. Next, a tree search algorithm is proposed to search for feasible JVCSs. A tree-based ensemble learning model is also used to extend the tree search algorithm to handle local optimality issues. The proposed approach is evaluated on the notional case of a communication system.