Static Data Race Detection for Interrupt-Driven Embedded Software

Gao

IIEEE Trans. Software Eng.

et al. 2022

Interrupt-driven programs are widely deployed in safety-critical embedded systems to perform hardware and resource dependent data operation tasks. The frequent use of interrupts in these systems can cause race conditions to occur due to interactions between application tasks and interrupt handlers (or two interrupt handlers). Numerous program analysis and testing techniques have been proposed to detect races in multithreaded programs. Little work, however, has addressed race condition problems related to hardware interrupts. In this paper, we present SDRacer, an automated framework that can detect, validate and repair race conditions in interrupt-driven embedded software. It uses a combination of static analysis and symbolic execution to generate input data for exercising the potential races. It then employs virtual platforms to dynamically validate these races by forcing the interrupts to occur at the potential racing points. Finally, it provides repair candidates to eliminate the detected races. We evaluate SDRacer on nine real-world embedded programs written in C language. The results show that SDRacer can precisely detect and successfully fix race conditions.

Section: Summary Of Resultsmentioning

confidence: 99%

Section: A Motivating Examplementioning

confidence: 99%

Automatic Detection, Validation, and Repair of Race Conditions in Interrupt-Driven Embedded Software

Gao

IIEEE Trans. Software Eng.

et al. 2022

“…In other work in this category Chen et al [7] develop a static analysis tool for race detection in binaries of interrupt-driven programs with interrupt priorities of upto two levels.…”

Section: Related Workmentioning

confidence: 99%

Static Race Detection for RTOS Applications

Tulsyan,

Pai,

D'Souza

2020

Preprint

We present a static analysis technique for detecting data races in Real-Time Operating System (RTOS) applications. These applications are often employed in safety-critical tasks and the presence of races may lead to erroneous behaviour with serious consequences. Analyzing these applications is challenging due to the variety of non-standard synchronization mechanisms they use. We propose a technique based on the notion of an "occurs-in-between" relation between statements. This notion enables us to capture the interplay of various synchronization mechanisms. We use a pre-analysis and a small set of not-occurs-in-between patterns to detect whether two statements may race with each other. Our experimental evaluation shows that the technique is efficient and effective in identifying races with high precision.

“…This improves runtime performance with high precision and developer productivity. Most of the related solutions for detecting data races do target low end interrupt based, non-multithreaded embedded systems [19,20,6,23]. Therefore, these solutions can not be directly applied to the multithreaded software for ARMv7.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, these solutions can not be directly applied to the multithreaded software for ARMv7. Moreover, Keul [12] and Chen [7] use static analysis techniques for race detection in interrupt-driven systems applications. Unfortunately, these techniques do not capture the runtime behavior of the program.…”

Section: Related Workmentioning

confidence: 99%

EmbedSanitizer: Runtime Race Detection Tool for 32-bit Embedded ARM

Matar

Tasiran

Unat

2017

Runtime Verification

We propose EmbedSanitizer, a tool for detecting concurrency data races in 32-bit ARM-based multithreaded C/C++ applications. Moreover, we motivate the idea of detecting data races in embedded systems software natively; without virtualization or emulation or use of alternative architecture. Detecting data races in applications on a target hardware provides more precise results and increased throughput and hence enhanced developer productivity. EmbedSanitizer extends ThreadSanitizer, a race detection tool for 64-bit applications, to do race detection for 32-bit ARM applications. We evaluate EmbedSanitizer using PARSEC benchmarks on an ARMv7 CPU with 4 logical cores and 933MB of RAM. Our race detection results precisely match with results when the same benchmarks run on 64-bit machine using ThreadSanitizer. Moreover, the performance overhead of EmbedSanitizer is relatively low as compared to running race detection on an emulator, which is a common platform for embedded software development.