Paper Info
Title
Learning User Preferences in Case-Based Software Reuse
Abstract
Case-Based Reasoning is a good framework for Software Reuse because it provides a flexible and powerful searching mechanism for software components. In a CBR system for software reuse it is important to learn the user preferences adapting the system software choices to the user. In a complex domain as software design, the similarity metric will also be complex, thus creating the necessity for a learning algorithm capable of weight learning. In this paper we present an evolutionary approach to similarity weight learning in a CBR system for software reuse. This approach is justified by the similarity metric complexity and recursive nature, which makes other learning methods to fail. We present experimental work showing the feasibility of this approach and we also present a parametric study, exploring several cross-over and mutation strategies.
Year
DOI
Venue
2000
10.1007/3-540-44527-7_11
EWCBR
Keywords
Field
DocType
software component,software reuse,learning user preferences,cbr system,similarity metric complexity,similarity metric,case-based software reuse,system software choice,software design,weight learning,learning method,evolutionary approach,case base reasoning
Computer science,Human–computer interaction,Artificial intelligence,Software metric,Software development,Software sizing,Distributed computing,Domain engineering,Feature-oriented domain analysis,Component-based software engineering,Software construction,Machine learning,Software framework
Conference
Volume
ISSN
ISBN
1898
0302-9743
3-540-67933-2
Citations 
PageRank 
References 
5
0.45
10
Authors
2
Name
Order
Citations
PageRank
Paulo Gomes1566.95
Carlos Bento220119.84