EDBT 2026 Demo / reviewers in the wild / expert
Naveen Valluri
dblp:43/3324
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software maintenance and evolution › software modularization
software clustering |
0.0 | 1 | 1999 | Automatic Software Clustering via Latent Semantic Analysis · ASE 1999 |
Software maintenance and evolution
software maintenance |
0.0 | 1 | 1999 | Automatic Software Clustering via Latent Semantic Analysis · ASE 1999 |
Software maintenance and evolution
software reuse |
0.0 | 1 | 1999 | Automatic Software Clustering via Latent Semantic Analysis · ASE 1999 |
Methods — techniques the papers use, named apart from their topics
latent semantic analysis · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Automated Clinical Shift Schedule Generation for Emergent Care Settings
Azadeh Mobasher, Naqi Khan, David Elop, Zach Griffin, Naveen Valluri |
AMIA | 5 |
| 1999 | Automatic Software Clustering via Latent Semantic AnalysisabstractThe 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 |
ASE | 2 |