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Naveen Valluri

dblp:43/3324 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none

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

Software engineering, systems software and programming languages · 1Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

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
Software maintenance and evolution · 100%

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

TopicWeightPapersLastEvidence papers
Software maintenance and evolution › software modularization
software clustering
0.011999
Automatic Software Clustering via Latent Semantic Analysis · ASE 1999
Software maintenance and evolution
software maintenance
0.011999
Automatic Software Clustering via Latent Semantic Analysis · ASE 1999
Software maintenance and evolution
software reuse
0.011999
Automatic Software Clustering via Latent Semantic Analysis · ASE 1999

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

latent semantic analysis · 0.0
YearPublicationVenuePosition
2021 Automated Clinical Shift Schedule Generation for Emergent Care Settings
Azadeh Mobasher, Naqi Khan, David Elop, Zach Griffin, Naveen Valluri
AMIA5
1999 Automatic Software Clustering via Latent Semantic Analysis
abstract
The paper describes the initial results of applying Latent Semantic Analysis (LSA) to program source code and associated documentation. Latent Semantic Analysis is a corpus based statistical method for inducing and representing aspects of the meanings of words and passages (of natural language) reflective in their usage. This methodology is assessed for application to the domain of software components (i.e., source code and its accompanying documentation). The intent of applying Latent Semantic Analysis to software components is to automatically induce a specific semantic meaning of a given component. Here LSA is used as the basis to cluster software components. Results of applying this method to the LEDA library and MINIX operating system are given. Applying Latent Semantic Analysis to the domain of source code and internal documentation for the support of software reuse is a new application of this method and a departure from the normal application domain of natural language.
Jonathan I. Maletic, Naveen Valluri
ASE2