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: 0205422
• 负责人: Mary Harrold
• 金额: $ 40万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-01 至 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0205422&HistoricalAwards=false
• 关键词: Collaborative; Research; ITR; Acquiring; Accurate

ABSTRACT0205422Harrold, Mary JGA Tech Res Corp - GITTITLE: Collaborative Research: ITR: Acquiring Accurate Dynamic Field Data 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 storage and 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 deployed software. 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 industrial software.

摘要：harrold， Mary JGA Tech Res Corp -标题：协作研究：ITR：使用轻量级仪器获取准确的动态场数据动态分析，如测试和概要分析，在软件质量保证（QA）的最新方法中起着关键作用。除了极少数例外，这些分析都是在内部的开发人员平台上使用开发人员提供的输入工作负载执行的。这种方法的缺点包括，结果根本不能被信任，不能告诉我们软件在该领域的实际表现。该项目的目标是为开发人员提供对其软件实际运行时行为的前所未有的洞察，允许开发人员（最终是软件本身）根据高度精确的字段数据更改、优化和调整软件。新平台在全球范围内全天候进行轻量级、协作式动态分析：(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)根据发现改变正在运行的程序实例；(4)重复该过程。七个关键的研究挑战构成了项目的核心：1。轻量级检测——开发对个人用户几乎透明的检测。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到原始问题的准确解决方案中。3. 可扩展性——开发存储和分析技术来处理我们期望遇到的高数据量。4. 异常检测——定义数据驱动技术来自动识别部署软件的异常行为。5. 隐私和安全——将隐私和安全保障纳入我们的数据收集和分析方法。6. 动态更新机制——开发技术，使运行时对工具的位置和功能以及软件本身的某些部分进行更改。7. 在工业软件上验证方法。