Paper Info
Title
A Hybrid Software Component Clustering and Retrieval Scheme Using an Entropy-Based Fuzzy k-Modes Algorithm
Abstract
Modern software development is currently seeking new paths to improve quality and meet time and cost constraints. Reuse of existing software components is considered one of these paths. However, this process experiences significant problems related to efficiently maintaining component repositories, and, moreover, providing the means to discover and retrieve the most suitable ones. This paper aims to provide a methodology to improve the component-based software development process. Specifically, its objective is to introduce an approach that reduces the time to locate suitable software components. The suggested methodology meets the requirements for the efficient searching of components in repositories and also addresses the need for adequate retrieval of the most suitable software components based on the needs of developers. To achieve this we employ a combination of partitional clustering algorithms borrowed from the field of computational intelligence and fuzzy logic thus creating a subset of the available components that are most suitable to the developers' preferences.
Year
DOI
Venue
2007
10.1109/ICTAI.2007.16
ICTAI
Keywords
Field
DocType
entropy,fuzzy logic,object-oriented programming,software quality,component-based software development,computational intelligence,entropy-based fuzzy k-modes algorithm,fuzzy logic,hybrid software component clustering,software component retrieval
Data mining,Computer science,Software system,Artificial intelligence,Software metric,Component-based software engineering,Software construction,Software verification and validation,Software sizing,Software development,Machine learning,Software framework
Conference
Volume
ISSN
ISBN
1
1082-3409
0-7695-3015-X
Citations 
PageRank 
References 
4
0.42
16
Authors
2
Name
Order
Citations
PageRank
Constantinos Stylianou1253.42
Andreas S. Andreou221636.65