CSR --- SMA: Predictive Testing of System Software

CSR --- SMA：系统软件的预测测试

• 批准号: 0720906
• 负责人: Koushik Sen
• 金额: $ 35万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0720906&HistoricalAwards=false
• 关键词: CSR; SMA; Predictive; Testing; System

Today's software systems suffer from poor reliability and security,with software errors costing the U.S. economy upwards of $60 billionannually. This situation is likely to get worse, as the complexity ofsoftware systems increases without a matching increase in theeffectiveness of software quality tools and techniques. The situationis particularly challenging for concurrent systems, which posesignificantly higher difficulties for software quality tools, yet arebecoming more widespread with the growing use of multicore machines.Static analysis software-quality tools are very precise but do notscale well to large codes. Testing is easy to use but for goodcoverage requires extensive test suites. We propose to bridge the gapbetween ad hoc testing and static analysis by combining them in a newscalable technique called predictive testing. Predictive testing usesstatic program analysis to maximize the effectiveness of a giventest-suite for finding in the testing stage bugs that could manifestin real production runs. Although predictive testing tools use complexstatic analysis and automated theorem proving techniques internally,all of this complexity is hidden from the user by a testing usagemodel. For this reason, we expect that such tools can be easilyintegrated into existing software engineering processes and will beusable even by unsophisticated developers.

当今的软件系统的可靠性和安全性差，软件错误的损失损失了美国经济的600亿美元。 随着软件系统的复杂性增加而没有匹配的软件质量工具和技术的效果，这种情况可能会变得更糟。这种情况对于并发系统特别具有挑战性，对于软件质量工具而言，这造成了更大的困难，但随着多功能机器的日益增长的使用而变得更加广泛。静态分析软件 - 质量工具非常精确，但对大型代码却不是很好。 测试易于使用，但要进行良好的测试需要广泛的测试套件。 我们建议通过将它们结合在一种称为预测性测试的新闻量表技术中，来弥合临时测试和静态分析之间的GAPBET。 预测性测试使用程序分析，以最大程度地提高赠送套件在测试阶段错误中发现的有效性，这些错误表现出可以表现出实际生产的运行。尽管预测性测试工具在内部使用复杂的静态分析并自动化定理证明技术，但是通过测试USAGEMODEL，所有这些复杂性都隐藏在用户中。因此，我们期望这样的工具可以轻松地集成到现有的软件工程流程中，即使是未教育的开发人员也可以敬畏。