EAGER: CCF: SHF: Mining the Execution History of a Software System to Infer the Safe Time for its Adaptation

随着软件工程师开发了新的技术来解决与现代软件系统的构造相关联的复杂性，对于在部署这些系统之后自动化和简化这些系统的管理的机制的同样迫切的需求已经上升，即，在运行时。这就要求开发（自）适应软件系统，这些系统能够在运行时修改它们的行为，以实现某些功能或服务质量目标。拟议的研究旨在开发一种替代方法，工程自适应软件，使用数据挖掘的方法来自动推导模型表达系统的组件之间的概率依赖关系。然后使用这些类型的模型来确保正在运行的软件中的更改不会产生危及系统的不一致性。的功能。指导这项研究的假设是，通过监控软件系统？的执行历史（例如，消息交换，方法调用）足够长的一段时间，有可能推断出一个相对准确的模型之间的相互作用和依赖关系的系统？的组件。所提出的方法将通过一套综合工具来实现。该研究将在受控实验室环境以及几个现实世界的应用程序中进行评估，这些应用程序代表了可以从这项研究中受益的系统类型。

As software engineers have developed new techniques to address the complexity associated with the construction of modern-day software systems, an equally pressing need has risen for mechanisms that automate and simplify the management of those systems after they are deployed, i.e., during runtime. This has called for the development of (self-)adaptive software systems, which are capable of modifying their behavior at runtime to achieve certain functional or quality of service objectives. The proposed research aims to develop an alternative approach to engineering adaptive software that uses a data mining approach to automatically derive models expressing probabilistic dependencies among the components of a system. These types of models are then used to ensure changes in the running software do not create inconsistencies that jeopardize the system?s functionality. The hypothesis guiding this research is that by monitoring a software system?s execution history (e.g., message exchange, method invocation) for a sufficiently long period of time, it is possible to infer a relatively accurate model of interactions and dependencies among the system?s components. The proposed approach will be realized via a suite of integrated tools. The research will be evaluated in both controlled laboratory setting, as well as several real-world applications that are representative of the kinds of systems that could benefit from this research.