一句话：提出Q学习算法高效推导完全/部分系统的同步序列，加速模型测试状态同步并缩短序列。
基于模型的测试是测试复杂系统的结构化方法，但扩展到大型系统需提升测试生成与执行效率。其中，将系统带到已知状态的状态同步代价最高，通常依赖同步序列：无论初始状态如何都能将系统带到预定状态的输入序列。系统可能是完全的或部分的，已有推导方法难以应对。本文提出一种新的Q学习算法，用于从完全或部分结构的系统中推导同步序列。评估表明，该算法比从完全系统推导同步序列的最快顺序算法更快、可处理更大系统，也比从部分系统推导同步序列的最新大规模并行算法更快、可扩展性更好，同时生成的同步序列更短。
Model-based testing is a structured method to test complex systems. Scaling up model-based testing to large systems requires improving the efficiency of various steps involved in testcase generation and more importantly, in test-execution. One of the most costly steps of model-based testing is to bring the system to a known state, best achieved through synchronising sequences. A synchronising sequence is an input sequence that brings a given system to a predetermined state regardless of system’s initial state. Depending on the structure, the system might be complete, i.e., all inputs are applicable at every state of the system. However, some systems are partial and in this case not all inputs are usable at every state. Derivation of synchronising sequences from complete or partial systems is a challenging task. In this paper, we introduce a novel Q-learning algorithm that can derive synchronising sequences from systems with complete or partial structures. The proposed algorithm is faster and can process larger systems than the fastest sequential algorithm that derives synchronising sequences from complete systems. Moreover, the proposed method is also faster and can process larger systems than the most recent massively parallel algorithm that derives synchronising sequences from partial systems. Furthermore, the proposed algorithm generates shorter synchronising sequences.
DOI 原文 ·
@inproceedings{paperbot6642,
title = {Efficient state synchronisation in model-based testing through reinforcement learning},
author = {Uraz Cengiz Turker and Robert M. Hierons and Mohammad Reza Mousavi and Ivan Y. Tyukin},
booktitle = {ASE},
year = {2021},
doi = {10.1109/ase51524.2021.9678566}
}