Abstract
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. [5]. We evaluate our technique on three popular RTOS kernels and show that it is effective in detecting races, many of them harmful, with a high rate of precision.
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Notes
- 1.
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Singh, A., Pai, R., D’Souza, D., D’Souza, M. (2019). Static Analysis for Detecting High-Level Races in RTOS Kernels. In: ter Beek, M., McIver, A., Oliveira, J. (eds) Formal Methods – The Next 30 Years. FM 2019. Lecture Notes in Computer Science(), vol 11800. Springer, Cham. https://doi.org/10.1007/978-3-030-30942-8_21
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