Changes, States, and Events: The Thread from Staticity to Dynamism in the Conceptual Modeling of Systems
August 11, 2020 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Sabah Al-Fedaghi
arXiv ID
2008.05017
Category
cs.SE: Software Engineering
Citations
6
Venue
arXiv.org
Last Checked
4 months ago
Abstract
This paper examines the concept of change in conceptual modeling. Change is inherent in the nature of things and has increasingly become a focus of much interest and investigation. Change can be modeled as a transition between two states of a finite state machine (FSM). This change represents an exploratory starting point in this paper. Accordingly, a sample FSM that models a car s transmission system is re-expressed in terms of a new modeling methodology called thinging machine (TM) modeling. Recasting the car-transmission model involves developing (1) an S model that captures the static aspects, (2) a D model that identifies states, and (3) a B model that specifies the behavior. The analysis progresses as follows. - S represents an atemporal diagrammatic description that embeds underlying compositions (static changes) from which the roots of system behavior can be traced. - S is broken down into multiple subsystems that correspond to static states (ordered constitutive components). - Introducing time into static states converts these states into events, and the behavior (B) model is constructed based on the chronology of these events. The analysis shows that FSM states are static (atemporal) changes that introduce temporal events as carriers of behavior. This result enhances the semantics of the concepts of change, states, and events in modeling and shows how to specify a system s behavior through its static description.
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