SHF: Small: Testing Large-Scale Database-Centric Applications

社交网络服务提供商、生命科学研究中心或安全机构等现代组织拥有前所未有的数据量。这些组织希望通过针对数据编写的软件应用程序来分析其数据。编写、测试和调试这样的数据密集型软件应用程序是非常复杂的。这项研究开发了处理这种复杂性的新技术。本研究的第一个目标是开发技术，可以自动找到一个现有的大规模数据集，允许程序员预测程序将如何表现在完整的数据集的代表性子集。直觉是，找到代表性数据子集以及在该子集上执行程序所需的资源可能比在完整数据集上运行应用程序低几个数量级。第二个研究目标是开发自动检查用户程序是否违反了提供MapReduce风格编程接口的数据处理系统所施加的正确性条件的技术。

Modern organizations such as social networking service providers, life-science research centers, or security agencies own an unprecedented amount of data. Such organizations want to analyze their data via software applications that are written against that data. Writing, testing, and debugging such data-intensive software applications is notoriously complex. This research develops novel techniques for dealing with this complexity. The first objective of this research is to develop techniques that can automatically find a representative subset of an existing large-scale data set that allows the programmer to predict how the program will behave on the full data set. The intuition is that the resources needed for finding a representative data subset plus executing the program on that subset can be orders of magnitude lower than running the application on the full data set. The second research objective is to develop techniques that automatically check if a user program violates the correctness conditions imposed by data processing systems that offer a MapReduce-style programming interface.