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

EAGER：CCF：SHF：挖掘软件系统的执行历史以推断其适应的安全时间

• 批准号: 1217503
• 负责人: Sam Malek
• 金额: $ 8万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-02-01 至 2014-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1217503&HistoricalAwards=false
• 关键词: EAGER; CCF; SHF; Mining; Execution

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.

由于软件工程师已经开发了新的技术来处理与现代软件系统构建相关的复杂性，在部署之后，也就是在运行期间，对那些系统的自动化和简化管理的机制的同样迫切的需求已经上升。这要求开发（自）适应软件系统，这些系统能够在运行时修改其行为，以实现某些功能或服务质量目标。提出的研究旨在开发一种工程自适应软件的替代方法，该方法使用数据挖掘方法自动导出表示系统组件之间概率依赖关系的模型。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。年代的功能。指导这项研究的假设是，通过监控一个软件系统？在足够长的一段时间内，如果对系统的执行历史（例如，消息交换、方法调用）进行分析，就有可能推断出系统之间的交互和依赖关系的相对准确的模型。年代组件。所提出的方法将通过一套集成工具来实现。这项研究将在受控的实验室环境中进行评估，同时也将在一些具有代表性的系统中进行评估，这些系统可能会从这项研究中受益。