Titlebook: Computer Safety, Reliability, and Security; 37th International C Barbara Gallina,Amund Skavhaug,Friedemann Bitsch Conference proceedings 20

臆斷

巧辦法

Iron: An Element Well-Fitted for its Task? challenging task. Examples of such dependencies across software-stack layers (e.g., between application and middleware layer) are common causes and failure propagation scenarios in which a failure of one software component propagates to another software component through shared services and/or comm

心痛

Chemistry and Physiology of the Transferrinsanalysis and fully-automated scalable safety verification of solid state interlocking (SSI) programs – a technology at the heart of many railway signalling solutions. The focus of the work is on making it easy for signalling engineers to use the developed technology and thus to help with its smooth

肥料

統(tǒng)治人類

Chemistry and Physiology of the Transferrins condition decision coverage (MCDC) criterion in system testing is an important testing technique for Boolean expression, as its usage mandated by safety standards such as DO-178 [.] (avionics) and ISO26262 [.] (automotive). In this paper, we develop an algorithm to generate optimal MCDC test suites

關(guān)節(jié)炎

https://doi.org/10.1007/978-94-009-7882-9ifying safety properties and physical tests for validating the models used for such verification. We follow a quantitative approach based on a probabilistic treatment of the different quantities in question. That is, we quantify the accuracy of a model in terms of its probabilistic prediction abilit

美學(xué)

Ostrich

Natural History of Honey Bee Learningystems and applications. Automotive vehicles now depend on software to the extent that manufacturers are turning to over-the-air updates for critical vehicle functionality. History shows that our software systems are most vulnerable to lapses in safety and dependability when they undergo change, and

LEERY

ABASH

Zooplankton Studies in the Indian Ocean of the embedded hardware. However, due to the vast computational requirements that deep neural networks exhibit, the use of traditional redundancy-based approaches for the detection and mitigation of random hardware errors leads to very inefficient systems. In this paper we present an efficient and