Paper Info
Title
Multi-variate principal component analysis of software maintenance effort drivers
Abstract
The global IT industry has already attained maturity and the number of software systems entering into the maintenance stage is steadily increasing. Further, the industry is also facing a definite shift from traditional environment of legacy softwares to newer softwares. Software maintenance (SM) effort estimation has become one of the most challenging tasks owing to the wide variety of projects and dynamics of the SM environment. Thus the real challenge lies in understanding the role of a large number of SM effort drivers. This work presents a multi-variate analysis of the effect of various drivers on maintenance effort using the Principal Component Analysis (PCA) approach. PCA allows reduction of data into a smaller number of components and its alternate interpretation by analysing the data covariance. The analysis is based on an available real life dataset of 14 drivers influencing the effort of 36 SM projects, as estimated by 6 experts.
Year
DOI
Venue
2010
10.1007/978-3-642-12165-4_22
ICCSA
Keywords
Field
DocType
software maintenance,smaller number,sm environment,available real life dataset,effort estimation,maintenance effort,sm effort driver,multi-variate principal component analysis,maintenance stage,large number,software maintenance effort driver,sm project,legacy software,variational analysis,principal component analysis,software systems
Data mining,Random variate,Mathematical optimization,Industrial engineering,Information technology,Computer science,Software system,Software maintenance,Maintenance stage,Principal component analysis,Software sizing,Covariance
Conference
Volume
ISSN
ISBN
6017
0302-9743
3-642-12164-0
Citations 
PageRank 
References 
0
0.34
12
Authors
2
Name
Order
Citations
PageRank
Ruchi Shukla1152.32
A. K. Misra200.34