Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 48 tok/s
Gemini 2.5 Pro 48 tok/s Pro
GPT-5 Medium 26 tok/s Pro
GPT-5 High 19 tok/s Pro
GPT-4o 107 tok/s Pro
Kimi K2 205 tok/s Pro
GPT OSS 120B 473 tok/s Pro
Claude Sonnet 4 37 tok/s Pro
2000 character limit reached

Tailoring the MontiArcAutomaton Component & Connector ADL for Generative Development (1511.05364v1)

Published 17 Nov 2015 in cs.SE

Abstract: Component&connector (C&C) architecture description languages (ADLs) combine component-based software engineering and model-driven engineering to increase reuse and to abstract from implementation details. Applied to robotics application development, current C&C ADLs often require domain experts to provide component behavior descriptions as programming language artifacts or as models of a-priori mixed behavior modeling languages. They are limited to specific target platforms or require extensive handcrafting to transform platform-independent software architecture models into platform-specific implementations. We have developed the MontiArcAutomaton framework that combines structural extension of C&C concepts with integration of application-specific component behavior modeling languages, seamless transformation from logical into platform-specific software architectures, and a-posteriori black-box composition of code generators for different robotics platforms. This paper describes the roles and activities for tailoring MontiArcAutomaton to application-specific demands.

Citations (5)

Summary

We haven't generated a summary for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Lightbulb On Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.