Paper Info
Title
Software quality knowledge discovery: a rough set approach
Abstract
This paper presents a practical knowledge discovery approach to software quality and resource allocation that incorporates recent advances in rough set theory, parameterized approximation spaces and rough neural computing. In addition, this research utilizes the results of recent studies of software quality measurement and prediction. A software quality measure quantifies the extent to which some specific attribute is present in a system. Such measurements are considered in the context of rough sets. . It has been shown rough setswork well in coping with the uncertainty in making decisions based on software engineering data. The thrust of this research is to provide a framework for making resource allocation decisions based on evaluation of various measurements of the complexity of software. Knowledge about software quality is gained during preprocessing during which, softwaremeasurements are analyzed using discretization techniques, genetic algorithms in deriving reducts, and in the derivation of training and testing sets, especially in the context of the Rough Sets Exploration System (RSES) developed by the logic group at the Institute ofMathematics at Warsaw University. The results of preprocessing provide a basis for rough neural computing and resource allocation decisions. Experiments have shown that both RSES and rough neural network models are effective in classifying software modules. The contribution of this paper is a presentation of a rough set based framework for making decisions about software quality and resource allocation.
Year
DOI
Venue
2002
10.1109/CMPSAC.2002.1045165
COMPSAC
Keywords
Field
DocType
data mining,genetic algorithms,neural nets,resource allocation,rough set theory,software engineering,software quality,genetic algorithms,knowledge discovery,neural network,preprocessing,resource allocation,rough set theory,software engineering,software quality
Data mining,Computer science,Rough set,Software,Knowledge extraction,Artificial intelligence,Software metric,Software construction,Software quality,Software sizing,Machine learning,Software mining
Conference
ISSN
ISBN
Citations 
0730-3157
0-7695-1727-7
2
PageRank 
References 
Authors
0.45
9
3
Name
Order
Citations
PageRank
S. Ramanna19218.42
James F. Peters21825184.11
Tae-Chon Ahn311616.49