Modular Verification of Interrupt-Driven Software
September 28, 2017 ยท Declared Dead ยท ๐ International Conference on Automated Software Engineering
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Chungha Sung, Markus Kusano, Chao Wang
arXiv ID
1709.10078
Category
cs.PL: Programming Languages
Cross-listed
cs.SE
Citations
22
Venue
International Conference on Automated Software Engineering
Last Checked
1 month ago
Abstract
Interrupts have been widely used in safety-critical computer systems to handle outside stimuli and interact with the hardware, but reasoning about interrupt-driven software remains a difficult task. Although a number of static verification techniques have been proposed for interrupt-driven software, they often rely on constructing a monolithic verification model. Furthermore, they do not precisely capture the complete execution semantics of interrupts such as nested invocations of interrupt handlers. To overcome these limitations, we propose an abstract interpretation framework for static verification of interrupt-driven software that first analyzes each interrupt handler in isolation as if it were a sequential program, and then propagates the result to other interrupt handlers. This iterative process continues until results from all interrupt handlers reach a fixed point. Since our method never constructs the global model, it avoids the up-front blowup in model construction that hampers existing, non-modular, verification techniques. We have evaluated our method on 35 interrupt-driven applications with a total of 22,541 lines of code. Our results show the method is able to quickly and more accurately analyze the behavior of interrupts.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Programming Languages
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
R.I.P.
๐ป
Ghosted
Glow: Graph Lowering Compiler Techniques for Neural Networks
R.I.P.
๐ป
Ghosted
Learnable Programming: Blocks and Beyond
R.I.P.
๐ป
Ghosted
Scenic: A Language for Scenario Specification and Scene Generation
R.I.P.
๐ป
Ghosted
Vandal: A Scalable Security Analysis Framework for Smart Contracts
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted