| Tartalom: |
http://hdl.handle.net/10890/15647 |
| Archívum: |
Műegyetem Digitális Archívum |
| Gyűjtemény: |
1. Tudományos közlemények, publikációk
Konferenciák gyűjteményei
BME MIT PhD Minisymposium
BME MIT PhD Minisymposium, 2021, 28th
|
| Cím: |
Towards Interactive Learning for Model-based Software Engineering
|
| Létrehozó: |
Barcsa-Szabó, Áron
Várady, Balázs
Farkas, Rebeka
Molnár, Vince
Vörös, András
|
| Dátum: |
2021-07-28T11:37:48Z
2021-07-28T11:37:48Z
2021
|
| Tartalmi leírás: |
Model-based technologies improve the efficiency of the design and development of IT systems by making it possible to automate verification, code generation and system analysis based on a formal model. A simple way of describing the behavior of systems is state-based modeling, which - due to the advancements of formal analysis techniques in recent years - can be widely and effectively utilized when analyzing systems. A possible way of synthesizing such models is to apply active automata learning algorithms. Acquiring a correct formal model of a system can be challenging because of the different theoretical and practical obstacles of both manual and automated approaches. We propose a semi-automated solution, that applies automata learning to provide an interactive environment for model development.
|
| Nyelv: |
angol
|
| Típus: |
könyvfejezet
|
| Formátum: |
application/pdf
|
| Azonosító: |
|
