Computer Program Decomposition and Dynamic/Behavioral Modeling
September 05, 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
2009.03669
Category
cs.SE: Software Engineering
Citations
8
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Decomposition, statically dividing a program into multiple units, is a common programming technique for realizing parallelism and refining programs. The decomposition of a sequential program into components is tedious, due to the limitations of program analysis and because sequential programs frequently employ inherently sequential algorithms. This paper contributes to this area of study by proposing a diagrammatic methodology to decompose a sequential program. The methodology involves visualizing the program in terms of a conceptual model called the thinging machine (TM) model. The TM diagram-based model establishes three levels of representation (1) a static description; (2) a dynamic representation; and (3) a behavioral model. The decomposition is performed in the last phase of modeling, according to the streams of events. This method is contrasted with formal decomposition specifications and compared with the typical decomposition of a C++ program. The results point to the viability of using TM for decomposing programs.
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