Automata learning enables model-based analysis of black-box systems by automatically constructing models from system observations, which are often collected via testing. The required testing budget to learn adequate models heavily depends on the applied learning and testing techniques.

Test cases executed for learning (1) collect behavioural information and (2) falsify learned hypothesis automata. Falsification test-cases are commonly selected through conformance testing. Active learning algorithms additionally implement test-case selection strategies to gain information, whereas passive algorithms derive models solely from given data. In an active setting, such algorithms require external test-case selection, like repeated conformance testing to extend the available data.

There exist various approaches to learning and conformance testing, where interdependencies among them affect performance. We investigate the performance of combinations of six learning algorithms, including a passive algorithm, and seven testing algorithms, by performing experiments using 153 benchmark models. We discuss insights regarding the performance of different configurations for various types of systems. Our findings may provide guidance for future users of automata learning. For example, counterexample processing during learning strongly impacts efficiency, which is further affected by testing approach and system type. Testing with the random Wp-method performs best overall, while mutation-based testing performs well on smaller models.

自动机学习通过根据系统观察（通常通过测试收集）自动构建模型，实现对黑盒系统的基于模型的分析。学习足够的模型所需的测试预算在很大程度上取决于所应用的学习和测试技术。

为学习而执行的测试用例 (1) 收集行为信息和 (2) 伪造学习的假设自动机。伪造测试用例通常是通过一致性测试来选择的。主动学习算法还实施测试用例选择策略来获取信息，而被动算法仅从给定数据中导出模型。在主动设置中，此类算法需要外部测试用例选择，例如重复一致性测试以扩展可用数据。

存在多种学习和一致性测试方法，它们之间的相互依赖性会影响性能。我们通过使用 153 个基准模型进行实验，研究了六种学习算法（包括被动算法）和七种测试算法的组合的性能。我们讨论有关各种类型系统的不同配置的性能的见解。我们的研究结果可能为未来的自动机学习用户提供指导。例如，学习过程中的反例处理强烈影响效率，而效率又进一步受到测试方法和系统类型的影响。使用随机 Wp 方法进行的测试总体表现最佳，而基于突变的测试在较小的模型上表现良好。