Assurance via workflow+ modelling and conformance
December 20, 2019 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Zinovy Diskin, Nicholas Annable, Alan Wassyng, Mark Lawford
arXiv ID
1912.09912
Category
cs.SE: Software Engineering
Citations
1
Venue
arXiv.org
Last Checked
5 months ago
Abstract
We propose considering assurance as a model management enterprise: saying that a system is safe amounts to specifying three workflows modelling how the safety engineering process is defined and executed, and checking their conformance. These workflows are based on precise data modelling as in functional block diagrams, but their distinctive feature is the presence of relationships between the output data of a process and its input data; hence, the name ``WorkflowPlus'', WF+ . A typical WP^+ model comprises three layers: (i) process and control flow, (ii) dataflow (with input-output relationships), and (iii) argument flow or constraint derivation. Precise dataflow modelling signifies a crucial distinction of (WP+)-based and GSN-based assurance, in which the data layer is mainly implicit. We provide a detailed comparative analysis of the two formalisms and conclude that GSN does not fulfil its promises.
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