With the growth in complexity of real-time embedded systems, there is an increasing need for tools and techniques to understand and compare the observed runtime behavior of a system with the expected one. Since many real-time applications require periodic interactions with the environment, one of the fundamental problems in guaranteeing their temporal correctness is to be able to infer the periodicity of certain events in the system. The practicability of a period inference tool, however, depends on both its accuracy and robustness (also its resilience) against noise in the output trace of the system, e.g., when the system trace is impacted by the presence of aperiodic tasks, release jitters, and runtime variations in the execution time of the tasks. This work (i) presents the first period inference framework that uses regression-based machine-learning (RBML) methods, and (ii) thoroughly investigates the accuracy and robustness of different families of RBML methods in the presence of uncertainties in the system parameters. We show, on both synthetically generated traces and traces from actual systems, that our solutions can reduce the error of period estimation by two to three orders of magnitudes w.r.t. the state of the art.

随着实时嵌入式系统复杂性的增加，越来越需要工具和技术来理解系统的观察到的运行时行为并将其与预期的运行时行为进行比较。由于许多实时应用程序需要与环境进行周期性交互，因此保证其时间正确性的基本问题之一是能够推断系统中某些事件的周期性。然而，周期推断工具的实用性取决于其准确性和鲁棒性（以及其抵抗系统输出轨迹中噪声的弹性），例如，当系统轨迹受到非周期任务、释放抖动、以及任务执行时间的运行时变化。这项工作 (i) 提出了第一个使用基于回归的机器学习 (RBML) 方法的周期推理框架，(ii) 彻底研究了不同系列 RBML 方法在系统参数存在不确定性的情况下的准确性和鲁棒性。我们在综合生成的迹线和实际系统的迹线上表明，我们的解决方案可以将周期估计的误差比现有技术降低两到三个数量级。