The SysML/KAOS Domain Modeling Approach
October 02, 2017 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Steve Tueno, RΓ©gine Laleau, Amel Mammar, Marc Frappier
arXiv ID
1710.00903
Category
cs.SE: Software Engineering
Citations
10
Venue
arXiv.org
Last Checked
4 months ago
Abstract
A means of building safe critical systems consists of formally modeling the requirements formulated by stakeholders and ensuring their consistency with respect to application domain properties. This paper proposes a metamodel for an ontology modeling formalism based on OWL and PLIB. This modeling formalism is part of a method for modeling the domain of systems whose requirements are captured through SysML/KAOS. The formal semantics of SysML/KAOS goals are represented using Event-B specifications. Goals provide the set of events, while domain models will provide the structure of the system state of the Event-B specification. Our proposal is illustrated through a case study dealing with a Cycab localization component specification. The case study deals with the specification of a localization software component that uses GPS,Wi-Fi and sensor technologies for the realtime localization of the Cycab vehicle, an autonomous ground transportation system designed to be robust and completely independent.
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