Scalable, Precise, and Effective Analyses for Detecting Race Conditions

用于检测竞争条件的可扩展、精确且有效的分析

• 批准号: 0541036
• 负责人: Michael Hicks
• 金额: $ 36万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0541036&HistoricalAwards=false
• 关键词: Scalable; Precise; Effective; Analyses; Detecting; Scalable; Precise; Effective; Analyses; Detecting

AWARD ABSTRACT0541036Michael HicksU of Maryland College ParkScalable, Precise, and Effective Analyses for Detecting Race Conditions Michael W. Hicks Jeffrey S. FosterMulti-threaded programming is an essential part of critical software such as operating systems and network servers. Multi-threaded programming is likely to become far more prevalent as hardware manufacturers are now building and shipping multi-CPU core machines. One common source of errors in multi-threaded programs is data races, which occur when two threads each concurrently access the same data.Race conditions are notoriously hard-to-find errors that can lead to incorrect behavior, data corruption, program failure, denial-of-service attacks, and/or security breaches. Because race conditions can be so pernicious, there has been widespread interest in developing tools for detecting and preventing them. However, these tools are still impractical. The research will develop tools that use static (whole-program) analysis to prove the absence of race conditions in C programs. The tools will be based on a common annotation and specification language that can describe commonly-used idioms for preventing data races. The goal is ultimately to develop techniques that scale to large software systems, and that are based on a sound foundation.

马里兰州公园公园的Michael Hicksu Affact Aspract0541036，用于检测比赛条件的有效分析，Michael W. Hicks Jeffrey S. Fostermulti-Threaded编程是关键软件的重要组成部分，例如操作系统和网络服务器。 随着硬件制造商现在正在建造和运送多CPU核心机器，多线程编程可能会变得更加普遍。 多线程程序中的一个常见错误来源是数据竞赛，当每个线程并同时访问相同的数据时，发生这种情况。 由于种族条件可能是如此有害，因此对开发用于检测和防止它们的工具的兴趣普遍存在。 但是，这些工具仍然不切实际。 该研究将开发使用静态（全程）分析的工具，以证明C计划中缺乏种族条件。 这些工具将基于通用的注释和规范语言，该语言可以描述预防数据种族的常用习语。 目标最终是开发扩展到大型软件系统的技术，并基于声音基础。