Algorithms for Runtime Generation of Homogeneous Classes of Objects
November 01, 2018 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Dmytro O. Terletskyi
arXiv ID
1811.07694
Category
cs.SE: Software Engineering
Cross-listed
cs.AI
Citations
3
Venue
arXiv.org
Last Checked
4 months ago
Abstract
This paper contains analysis of main modern approaches to dynamic code generation, in particular generation of new classes of objects during program execution. The main attention was paid to universal exploiters of homogeneous classes of objects, which were proposed as a part of such knowledge representation model as object-oriented dynamic networks, as the tools for generation of new classes of objects in program runtime. As the result, algorithms for implementation of such universal exploiters of classes of objects as union, intersection, difference and symmetric difference were developed. These algorithms can be used knowledge-based intelligent systems, which are based on object-oriented dynamic networks, and they can be adapted for some object-oriented programming languages with powerful metaprogramming opportunities.
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