Collaborative Research: ITR: Acquiring Accurate Dynamic Field Data Using Lightweight Instrumentation

合作研究：ITR：使用轻型仪器获取准确的动态场数据

• 批准号: 0205265
• 负责人: Adam Porter
• 金额: --
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0205265&HistoricalAwards=false
• 关键词: Collaborative; Research; ITR; Acquiring; Accurate

0205625,0205118, 0205429, 0205627 TITLE: Collaborative Research: ITR: Acquiring Accurate Dynamic FieldData Using Lightweight InstrumentationDynamic analyses, such as testing and profiling, play a key role in state-of-art approaches to software quality assurance (QA). With rare exception, these analyses are performed in-house, on developerplatforms, using developer-provided input workloads. Shortcomings of this approach include that the results simply cannot be trusted to tell us how the software actually performs in the field.The project goal is to give developers unprecedented insight into the actual runtime behavior of their software, allowing developers (and ultimately the software itself) to change, optimize, and adapt the software based on highly accurate field data. Lightweight, collaborative dynamic analyses conducted around-the-world and around-the-clock form the new platform: (1) lightly instrument fielded software (i.e., each program copy performs a small part of the analysis) (2) collect the partial data from many instances of the software, fusing it to conduct the complete analysis, (3) change the running program instances based on the findings and (4) repeat the process.Seven critical research challenges form the core of the project: 1. Lightweight instrumentation--Develop instrumentation that is virtually transparent to individual users. 2. Compositional analysis techniques--Develop distributed analysis techniques that decomposetraditional analyses into smaller steps, distribute the steps among multiple users, and then fuse each user's results into an accurate solution to the original problem. 3. Scalability--Develop storageand analysis techniques to deal with the high data volumes we expect to encounter. 4. Anomaly Detection--Define data-driven techniques to automatically identify anomalous behaviors of deployedsoftware. 5. Privacy and Security--Incorporate privacy and security safeguards into our data collection and analysis approaches. 6. Dynamic updating mechanisms--Develop techniques to make runtime changes to the location and function of instrumentation, and to parts of the software itself. 7. Validate approach on industrialsoftware.

0205625,0205118, 0205429, 0205627 标题：协作研究：ITR：使用轻量级仪器获取准确的动态现场数据动态分析（例如测试和分析）在最先进的软件质量保证 (QA) 方法中发挥着关键作用。 除了极少数例外，这些分析都是在开发人员平台上使用开发人员提供的输入工作负载在内部执行的。这种方法的缺点包括我们根本无法相信结果会告诉我们软件在现场的实际执行情况。该项目的目标是让开发人员能够前所未有地深入了解其软件的实际运行时行为，从而允许开发人员（最终是软件本身）基于高度准确的现场数据来更改、优化和调整软件。在世界各地、全天候进行的轻量级协作动态分析形成了新平台：(1) 轻型仪器现场软件（即每个程序副本执行一小部分分析）(2) 从软件的许多实例中收集部分数据，将其融合以进行完整的分析，(3) 根据结果更改正在运行的程序实例，(4) 重复该过程。七个关键研究挑战构成了该项目的核心：1. 轻量级仪器——开发对个人用户几乎透明的仪器。 2. 组合分析技术——开发分布式分析技术，将传统分析分解为更小的步骤，将这些步骤分配给多个用户，然后将每个用户的结果融合为原始问题的准确解决方案。 3. 可扩展性——开发存储和分析技术来处理我们预计会遇到的大量数据。 4. 异常检测——定义数据驱动技术来自动识别已部署软件的异常行为。 5. 隐私和安全——将隐私和安全保障措施纳入我们的数据收集和分析方法中。 6. 动态更新机制——开发对仪器的位置和功能以及软件本身的部分进行运行时更改的技术。 7. 验证工业软件的方法。