Diversity and Feedback in Random Testing for Systems Software

系统软件随机测试的多样性和反馈

• 批准号: 1217824
• 负责人: Alex Groce
• 金额: $ 24.22万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1217824&HistoricalAwards=false
• 关键词: Diversity; Feedback; Random; Testing; Systems

Testing is an extremely important part of any software development effort, especially for programs that have access to sensitive resources (personal information, sensor data, etc.) and that can be reached through the Internet. The PIs' work will improve the state of the art in software testing for mobile applications running on the open source Android platform, resulting in fewer bugs and reduced testing effort.The foundation of the PIs' work is random testing, where random numbers are used as inputs to an algorithm for constructing test cases. Although random testing has been shown to be highly effective for discovering serious bugs in complex software systems, it suffers from various problems including the fact that it is very difficult to engineer a random tester that doesn't spend a lot of time re-exploring the same application behaviors over and over again. The PIs will build upon their "swarm testing" work, which has been shown to be an inexpensive way to increase the diversity of random test cases, and also to increase their effectiveness in discovering bugs. Additionally, the PIs are investigating how to marry random testing with modern symbolic execution methods, and how to use feedback from executions of the software under test in order to improve the efficacy of random testing.The development of more efficient and effective testing techniques and tools will lower the cost and raise the quality of software. Test coverage is a challenging, open problem that is being addressed here in a novel way.

测试是任何软件开发工作中极其重要的一部分，特别是对于可以访问敏感资源（个人信息，传感器数据等）的程序。并且可以通过互联网访问。PI的工作将提高运行在开源Android平台上的移动的应用程序的软件测试的最新水平，从而减少错误并减少测试工作。PI工作的基础是随机测试，其中随机数被用作构建测试用例的算法的输入。尽管随机测试已经被证明对于发现复杂软件系统中的严重错误是非常有效的，但它存在各种问题，包括很难设计出一个不花费大量时间一遍又一遍地重新探索相同应用程序行为的随机测试人员。PI将建立在他们的“群测试”工作的基础上，这已经被证明是一种廉价的方法来增加随机测试用例的多样性，并提高他们发现bug的效率。此外，研究员们正研究如何将随机测试与现代符号执行方法结合起来，以及如何利用被测软件执行的反馈来提高随机测试的效率，开发更有效的测试技术和工具，以降低成本，提高软件质量。 测试覆盖率是一个具有挑战性的、开放的问题，这里以一种新颖的方式来解决这个问题。