Integrated Observation and Modeling Techniques to Support Adaptation and Evolution of Software Systems

支持软件系统适应和演化的集成观测和建模技术

• 批准号: 221686643
• 负责人: Professor Dr. Wilhelm Hasselbring
• 金额: --
• 依托单位: Lehrstuhl für Software-Entwurf und Qualität
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2012
• 资助国家: 德国
• 起止时间: 2011-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/221686643?language=en
• 关键词: Integrated; Observation; Modeling; Techniques; Support

The increased adoption of service-oriented technologies and cloud computing creates new challenges for the adaptation and evolution of long-living software systems. Software services and cloud platforms are owned and maintained by independent parties. Software engineers and system operators of long-living software systems only have limited visibility and control over those third-party elements. Traditional monitoring provides software engineers and system operators with execution observation data which are used as basis to detect anomalies. If the services and the cloud platform are not owned and controlled by the engineers of the software systems, monitoring the execution of the software system is not straightforward.The aim of the iObserve project is to develop and validate advanced techniques which empower the system engineers to observe and detect anomalies of the execution of software systems they do not fully own and control. It will extend and integrate previous work on adaptive monitoring, online testing and benchmarking and will use models@runtime as means to adjust the observation and anomaly detection techniques during system operation. To demonstrate the feasibility and potential benefits gained and for providing feedback to guide the research, the results will be continuously evaluated using an established research benchmark (CoCoME) as well as an industry-driven open-source application (Eclipse Skalli) that runs on a cloud platform.

越来越多地采用面向服务的技术和云计算为长寿软件系统的适应和发展带来了新的挑战。软件服务及云平台由独立人士拥有及维护。长期软件系统的软件工程师和系统操作员对这些第三方元素的可见性和控制力有限。传统的监控为软件工程师和系统操作员提供了执行观察数据，这些数据被用作检测异常的基础。如果服务和云平台不是由软件系统的工程师拥有和控制的，那么监控软件系统的执行就不那么简单。iObserve项目的目的是开发和验证先进的技术，使系统工程师能够观察和检测他们不完全拥有和控制的软件系统的执行异常。它将扩展和整合以前在自适应监测、在线测试和基准测试方面的工作，并将使用运行时模型作为在系统运行期间调整观察和异常检测技术的手段。为了证明所获得的可行性和潜在利益，并提供反馈以指导研究，将使用既定的研究基准（CoCoME）以及在云平台上运行的行业驱动的开源应用程序（Eclipse Skalli）对结果进行持续评估。