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

Sebastian Elbaum内布拉斯加大学林肯分校职业生涯：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，更不用说练习，他们的软件面临的大量潜在场景。 相反，他们根据软件发布后将如何使用的假设来分配有限的资源。然而，内部活动与软件如何在外地使用之间缺乏联系可能导致不准确的假设，导致软件质量和可靠性在系统寿命期间下降。该项目将通过开发占用空间小的特征分析技术来捕获现场数据，并开发能够利用这些信息的自适应测试技术，来应对这些挑战。这两个主要目标是：1）为大型用户池设计分析机制和资源认知监控方案，以减少执行开销; 2）开发自适应测试技术，不断表征软件行为，以识别测试不足，重新调整测试资源，并扩展现有的测试套件，以克服其局限性。 将定期进行实证研究，进行技术评估和探索。这项工作的教育组成部分的目的是把现实世界的验证问题的早期升值本科课堂，并准备学生精通软件质量和经验验证。

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.