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Marsha Kimelman

dblp:85/7993 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2010
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Requirements engineering and software design · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Requirements engineering and software design › model-driven engineering › model management
diagram matching
0.112010
Bayesian Approaches to Matching Architectural Diagrams · IEEE Trans. Software Eng. 2010
Requirements engineering and software design
model-driven engineering
0.112010
Bayesian Approaches to Matching Architectural Diagrams · IEEE Trans. Software Eng. 2010
Requirements engineering and software design
software architecture
0.112010
Bayesian Approaches to Matching Architectural Diagrams · IEEE Trans. Software Eng. 2010

Methods — techniques the papers use, named apart from their topics

search algorithm · 0.1graph matching · 0.1bayesian methods · 0.1
YearPublicationVenuePosition
2010 Bayesian Approaches to Matching Architectural Diagrams
abstract
IT system architectures and many other kinds of structured artifacts are often described by formal models or informal diagrams. In practice, there are often a number of versions of a model or diagram, such as a series of revisions, divergent variants, or multiple views of a system. Understanding how versions correspond or differ is crucial, and thus, automated assistance for matching models and diagrams is essential. We have designed a framework for finding these correspondences automatically based on Bayesian methods. We represent models and diagrams as graphs whose nodes have attributes such as name, type, connections to other nodes, and containment relations, and we have developed probabilistic models for rating the quality of candidate correspondences based on various features of the nodes in the graphs. Given the probabilistic models, we can find high-quality correspondences using search algorithms. Preliminary experiments focusing on architectural models suggest that the technique is promising.
Doug Kimelman, Marsha Kimelman, David Mandelin, Daniel M. Yellin
IEEE Trans. Software Eng.2