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David Garlan
Researcher at Carnegie Mellon University
Publications - 393
Citations - 27897
David Garlan is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Software architecture & Software system. The author has an hindex of 68, co-authored 378 publications receiving 26980 citations. Previous affiliations of David Garlan include Tektronix & Software Engineering Institute.
Papers
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Proceedings ArticleDOI
Using parameters in architectural views to support heterogeneous design and verification
TL;DR: This paper introduces the use of logical constraints over parameters in the architectural views to represent the conditions under which the specifications verified for each model are true and imply the system-level specification.
Journal ArticleDOI
Making formal methods education effective for professional software engineers
TL;DR: This paper details some of the problems with the approach taken by most programs to teach formal techniques for software development in a separate course on formal methods, and describes an alternative in which formal methods are integrated across the curriculum.
Book ChapterDOI
Improving architecture-based self-adaptation using preemption
TL;DR: This paper improves on existing practice through an approach in which adaptations can be preempted to allow for other time-critical adaptations to be scheduled, based on an algorithm that maximizes time-related utility for a set of concurrently executing adaptations.
Posted Content
Machine Learning Meets Quantitative Planning: Enabling Self-Adaptation in Autonomous Robots
TL;DR: In this article, the authors explore an approach that uses machine learning to find Pareto-optimal configurations without needing to explore every configuration and restrict the search space to such configurations to make planning tractable.
Proceedings ArticleDOI
Kubow: an architecture-based self-adaptation service for cloud native applications
TL;DR: Kubow is presented, an extensible architecture-based self-adaptation service for cloud native applications that was implemented by customizing and extending the Rainbow self- Adaptation framework with support for Docker containers and Kubernetes.