A UCB-Based Tree Search Approach to Joint Verification-Correction Strategy for Large-Scale Systems
针对大规模系统开发中联合验证与校正活动的规划难题，提出一种基于UCB的树搜索方法，通过简化决策问题并引入集成学习模型，搜索近似最优策略，并在通信系统案例中验证效果。
Verification planning is a sequential decision-making problem that specifies a set of verification activities (VAs) and correction activities (CAs) 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 challenging, especially for large-scale 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 (UCBRBs) 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.