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

SHF：小型：测试大规模以数据库为中心的应用程序

• 批准号: 1117369
• 负责人: Christoph Csallner
• 金额: $ 49.8万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117369&HistoricalAwards=false
• 关键词: SHF; Small; Testing; Large; Scale

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.

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空前数量的数据。这些组织希望通过针对该数据编写的软件应用程序分析其数据。众所周知，编写，测试和调试此类数据密集型软件应用程序非常复杂。这项研究开发了用于处理这种复杂性的新技术。这项研究的第一个目的是开发可以自动找到现有大规模数据集的代表性子集的技术，该数据集使程序员可以预测程序在完整数据集上的行为。直觉是，在该子集上找到代表性数据子集以及执行程序所需的资源可能比在完整数据集中运行应用程序的数量级低。第二个研究目标是开发技术，该技术会自动检查用户程序是否违反了提供MAPREDUCE式编程接口的数据处理系统所施加的正确性条件。