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Deva Kumar Deeptimahanti

dblp:55/7880 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2017
0000-0002-1812-8550ORCID · corroborated

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

Software engineering, systems software and programming languages · 2 · 1 first-author

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 1 heaviest of 1, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Requirements engineering and software design › requirements specification
natural language requirements
0.112009
An Automated Tool for Generating UML Models from Natural Language Requirements · ASE 2009

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

natural language processing · 0.1
YearPublicationVenuePosition
2017 An empirical study of data decomposition for software parallelization
Anne Meade, Deva Kumar Deeptimahanti, Jim Buckley, J. J. Collins
J. Syst. Softw.2
2009 An Automated Tool for Generating UML Models from Natural Language Requirements
abstract
This paper describes a domain independent tool, named, UML Model Generator from Analysis of Requirements (UMGAR), which generates UML models like the Use-case Diagram, Analysis class model, Collaboration diagram and Design class model from natural language requirements using efficient Natural Language Processing (NLP) tools. UMGAR implements a set of syntactic reconstruction rules to process complex requirements into simple requirements. UMGAR also provides a generic XMI parser to generate XMI files for visualizing the generated models in any UML modeling tool. With respect to the existing tools in this area, UMGAR provides more comprehensive support for generating models with proper relationships, which can be used for large requirement documents.
Deva Kumar Deeptimahanti, Muhammad Ali Babar 0001
ASE1