CAREER: Leveraging Field Data to Test Highly-Configurable and Rapidly-Evolving Pervasive Systems

职业：利用现场数据测试高度可配置且快速发展的普及系统

• 批准号: 0347518
• 负责人: Sebastian Elbaum
• 金额: $ 40万
• 依托单位: University of Nebraska-Lincoln
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-03-15 至 2010-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0347518&HistoricalAwards=false
• 关键词: CAREER; Leveraging; Field; Data; Test

ABSTRACT0347518Sebastian ElbaumUniversity of Nebraska-LincolnCAREER: Leveraging Field Data to Test Highly-Configurable and Rapidly Evolving Pervasive SystemsSoftware test engineers cannot predict, much less exercise, the overwhelming number of potential scenarios faced by their software. Instead, they allocate their limited resources based on assumptions about how the software will be employed after release. Yet, the lack of connection between in-house activities and how the software is employed in the field can lead to inaccurate assumptions, resulting in decreased software quality and reliability over the system's lifetime. This project will address those challenges by developing profiling techniques with a small footprint to capture field data and adaptive testing techniques that can leverage that information. The two primary objectives are: 1) to design profiling mechanisms for large pools of users andresource-cognizant monitoring schemes to reduce execution overhead, and 2) to develop adaptive testing techniques that continuously characterize software behavior to identify testing inadequacies, realign testingresources, and extend existing test suites to overcome their limitations. Empirical studies will be regularly employed for technique evaluation and exploration. The educational component of this work aims at bringing an earlier appreciation of the real-world validation problems to the undergraduate classroom and prepare students well-versed in software quality and empirical validation.

ABSTRACT0347518塞巴斯蒂安·埃尔鲍姆内布拉斯加大学林肯分校：利用现场数据测试高度可配置和快速发展的普及系统软件测试工程师无法预测他们的软件面临的大量潜在场景，更不用说实践了。相反，他们根据对软件发布后如何使用的假设来分配有限的资源。然而，内部活动与软件在外地的使用方式之间缺乏联系可能会导致不准确的假设，从而降低系统生命周期内的软件质量和可靠性。该项目将通过开发占用空间较小的概况分析技术来获取现场数据，并开发能够利用这些信息的适应性测试技术来应对这些挑战。两个主要目标是：1)为大量用户和资源认知监控方案设计分析机制，以减少执行开销；2)开发自适应测试技术，不断地表征软件行为，以识别测试不足，重新调整测试资源，并扩展现有测试套件，以克服其局限性。将定期进行实证研究，以进行技术评估和探索。这项工作的教育部分旨在将对真实世界验证问题的更早认识带入本科生课堂，并使精通软件质量和经验验证的学生做好准备。