Testing is a crucial phase in the development of complex systems, and this has led to interest in automated test generation techniques based on state-based models. Many approaches use models that are types of finite state machine (FSM). Corresponding test generation algorithms typically require that certain test components, such as reset sequences (RSs) and preset distinguishing sequences (PDSs), have been produced for the FSM specification. Unfortunately, the generation of RSs and PDSs is computationally expensive, and this affects the scalability of such FSM-based test generation algorithms. This paper addresses this scalability problem by introducing a reinforcement learning framework: the $\mathcal{Q}$ -Graph framework for MBT. We show how this framework can be used in the generation of RSs and PDSs and consider both (potentially partial) timed and untimed models. The proposed approach was evaluated using three types of FSMs: randomly generated FSMs, FSMs from a benchmark, and an FSM of an Engine Status Manager for a printer. In experiments, the proposed approach was much faster and used much less memory than the state-of-the-art methods in computing PDSs and RSs.

中文翻译：

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使用强化学习加速基于有限状态机的测试

测试是复杂系统开发的关键阶段，这引发了人们对基于状态模型的自动化测试生成技术的兴趣。许多方法使用有限状态机 (FSM) 类型的模型。相应的测试生成算法通常要求已经为FSM规范生成某些测试组件，例如复位序列(RS)和预设区分序列(PDS)。不幸的是，RS 和 PDS 的生成计算成本很高，这会影响此类基于 FSM 的测试生成算法的可扩展性。本文通过引入强化学习框架来解决这个可扩展性问题：$\mathcal{Q}$ -MBT 的图形框架。我们展示了如何使用该框架来生成 RS 和 PDS，并考虑（可能部分）定时和不定时模型。使用三种类型的 FSM 来评估所提出的方法：随机生成的 FSM、来自基准的 FSM 以及打印机引擎状态管理器的 FSM。在实验中，所提出的方法比计算 PDS 和 RS 的最先进方法要快得多，并且使用的内存要少得多。