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RRF: A Race Reproduction Framework for Use in Debugging Process-Level Races

机译：RRF：用于调试过程级竞赛的竞赛复制框架

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摘要

Process-level races are endemic in modern systems. These races are difficult to debug because they are sensitive to execution events such as interrupts and scheduling. Unless a process interleaving that can result in the race can be found, it cannot be reproduced and cannot be corrected. In practice, however, the number of interleavings that can occur among processes in practice is large, and the patterns of interleavings can be complex. Thus, approaches for reproducing process-level races to date are often ineffective. In this paper, we present RRF, a race reproduction framework that can help software engineers reproduce reported process-level races, enabling them to potentially debug these races. RRF performs a hybrid analysis by leveraging existing static program analysis tools, dynamic kernel event reporting tools, and yield points to provide the observability and controllability needed to reproduce races. We conducted an empirical study to evaluate RRF, our results show that RRF can be effective for reproducing races.
机译：在现代系统中，过程级竞赛是地方性的。这些种族很难调试，因为它们对诸如中断和调度之类的执行事件敏感。除非找到可能导致比赛的过程交错，否则无法复制和纠正该过程。然而，实际上，在实践中的过程之间可能发生的交织的数量很大，并且交织的模式可能很复杂。因此，迄今为止再现过程级竞赛的方法通常是无效的。在本文中，我们介绍了RRF，它是一个竞赛复制框架，可以帮助软件工程师复制报告的过程级竞赛，使他们有可能调试这些竞赛。 RRF通过利用现有的静态程序分析工具，动态内核事件报告工具和屈服点来执行混合分析，以提供重现种族所需的可观察性和可控性。我们进行了一项实证研究，以评估RRF，我们的结果表明RRF可以有效地复制种族。

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