The understanding of the behavioral aspects of a software system is an essential enabler for many software engineering activities, such as adaptation. This involves collecting runtime data from the system so that it is possible to analyze the collected data to guide actions upon the system. Consequently, software monitoring imposes practical challenges because it is often done by intercepting the system execution and recording gathered information. Such monitoring may degrade the performance and disrupt the system execution to unacceptable levels. In this paper, we introduce a two-phase monitoring approach to support the monitoring step in adaptive systems. The first phase collects lightweight coarse-grained information and identifies relevant parts of the software that should be monitored in detail based on a provided domain-specific language. This language is informed by a systematic literature review. The second phase collects relevant and fine-grained information needed for deciding whether and how to adapt the managed system. Our approach is implemented as a framework, called Tigris, that can be seamlessly integrated into existing software systems to support monitoring-based activities. To validate our proposal, we instantiated Tigris to support an application-level caching approach, which adapts caching decisions of a software system at runtime to improve its performance.

对软件系统行为方面的理解是许多软件工程活动（例如适应）的重要推动力。这涉及从系统收集运行时数据，以便可以分析收集的数据以指导对系统的操作。因此，软件监视带来了实际的挑战，因为它通常是通过拦截系统执行并记录收集的信息来完成的。这种监视可能会降低性能，并使系统执行中断到不可接受的水平。在本文中，我们介绍了一种两阶段监视方法来支持自适应系统中的监视步骤。第一阶段收集轻量级的粗粒度信息，并根据提供的特定于域的语言，确定应详细监视的软件相关部分。该语言是通过系统的文献综述获得的。第二阶段收集确定和是否适应管理型系统所需的相关且细粒度的信息。我们的方法是作为名为Tigris的框架实施的，可以将其无缝集成到现有软件系统中，以支持基于监视的活动。为了验证我们的建议，我们实例化了Tigris以支持应用程序级缓存方法，该方法在运行时调整软件系统的缓存决策以提高其性能。可以无缝集成到现有软件系统中，以支持基于监视的活动。为了验证我们的建议，我们实例化了Tigris以支持应用程序级缓存方法，该方法在运行时调整软件系统的缓存决策以提高其性能。可以无缝集成到现有软件系统中，以支持基于监视的活动。为了验证我们的建议，我们实例化了Tigris以支持应用程序级缓存方法，该方法在运行时调整软件系统的缓存决策以提高其性能。