SHF:Small:Collaborative Research: Compositional Verification of Heterogeneous Software Protocol Stacks

SHF:Small:协作研究：异构软件协议栈的组合验证

• 批准号: 1422705
• 负责人: Limin Jia
• 金额: $ 24.7万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1422705&HistoricalAwards=false
• 关键词: SHF; Small; Collaborative; Research; Compositional

Developing a software protocol stack is a complex task with many parties and products involved over multiple years. There are many issues that have to be resolved in the process, and it is incredibly hard to get all of them correct for all platforms and all possible states of a system. Hence, there are numerous examples of serious errorsthat ended up in production, where the cost of fixing them is high. Software protocol stacks are widely used in many domains, and rigorous methods to improve their reliability would have a very broad impact. The project develops a more rigorous approach to developing and maintaining such complex software protocol stacks.In particular, formal methods are used to find errors early through rigorous verification and testing, as well as to assist the analysis in locating reported errors. The approach is based on automatically learning abstract and succinct protocol models at different layers of the target software stack. Compositional reasoning is then applied to find errors effectively and precisely. The generated models are leveraged to help with error localization and diagnosis. The usability of the approach is assessed through a real-life case study on the Android Bluetooth software stack.

开发软件协议栈是一项复杂的任务，涉及许多方面和产品多年。在这个过程中有许多问题需要解决，并且很难在所有平台和系统的所有可能状态下都正确处理所有问题。因此，有许多严重错误的例子最终出现在生产中，修复它们的成本很高。软件协议栈广泛应用于许多领域，提高其可靠性的严格方法将产生非常广泛的影响。该项目开发了一种更严格的方法来开发和维护这种复杂的软件协议栈，特别是使用正式方法通过严格的验证和测试及早发现错误，并协助分析确定报告的错误。该方法是基于自动学习抽象和简洁的协议模型在不同层次的目标软件栈。然后应用组合推理来有效地和精确地发现错误。生成的模型被用来帮助错误定位和诊断。该方法的可用性进行了评估，通过现实生活中的案例研究的Android蓝牙软件栈。