We propose a static analysis based approach for detecting high-level races in RTOS kernels popularly used in safety-critical embedded software. High-Level races are indicators of atomicity violations and can lead to erroneous software behaviour with serious consequences. Hitherto techniques for detecting high-level races have relied on model-checking approaches, which are inefficient and apriori unsound. In contrast we propose a technique based on static analysis that is both efficient and sound. The technique is based on the notion of disjoint blocks recently introduced in Chopra et al. (In: Proceedings of 28th European symposium on programming (ESOP), Prague, Czech Republic. LNCS, vol 11423, pp 1–27. Springer, 2019). We evaluate our technique on four popular RTOS kernels and show that it is effective in detecting races, many of them harmful, with a high rate of precision.

中文翻译：

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用于检测 RTOS 内核中的高级竞争的静态分析

我们提出了一种基于静态分析的方法，用于检测安全关键嵌入式软件中普遍使用的 RTOS 内核中的高级竞争。高级别竞争是违反原子性的指标，可能导致错误的软件行为并带来严重后果。迄今为止，检测高水平种族的技术依赖于模型检查方法，这种方法效率低下且先验不可靠。相比之下，我们提出了一种基于静态分析的技术，既高效又可靠。该技术基于 Chopra 等人最近引入的不相交块的概念。（在：第 28 届欧洲编程研讨会 (ESOP) 的会议记录，捷克共和国布拉格。LNCS，第 11423 卷，第 1-27 页。施普林格，2019 年）。我们在四种流行的 RTOS 内核上评估了我们的技术，并表明它可以有效地检测竞争，其中许多是有害的，