The CLEARSY Safety Platform: 5 Years of Research, Development and Deployment
May 13, 2020 Β· Declared Dead Β· π Science of Computer Programming
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Thierry Lecomte, David Deharbe, Paulin Fournier, Marcel Oliveira
arXiv ID
2005.10662
Category
cs.SE: Software Engineering
Citations
2
Venue
Science of Computer Programming
Last Checked
4 months ago
Abstract
The CLEARSY Safety Platform (CSSP) was designed to ease the development of safety critical systems and to reduce the overall costs (development, deployment, and certification) under the pressure of the worldwide market. A smart combination of hardware features (double processor) and formal method (B method and code generators) was used to produce a SIL4-ready platform where safety principles are built-in and cannot be altered by the developer. Summarizing a 5-year return of experience in the effective application in the railways, this article explains how this approach is a game-changer and tries to anticipate the future of this platform for safety critical systems. In particular, the education of future engineers and the seamless integration in existing engineering processes with the support of Domain Specific Languages are key topics for a successful deployment in other domains. DSL like Robosim to program mobile robots and relay circuits to design railway signalling systems are connected to the platform.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted