DocumentCode
1678898
Title
Software quality prediction using median-adjusted class labels
Author
Pizzi, Nicolino J. ; Summers, Arthur R. ; Pedrycz, Witold
Author_Institution
Inst. for Biodiagnostics, Nat. Res. Council of Canada, Winnipeg, Man., Canada
Volume
3
fYear
2002
fDate
6/24/1905 12:00:00 AM
Firstpage
2405
Lastpage
2409
Abstract
Software metrics aid project managers in predicting the quality of software systems. A method is proposed using a neural network classifier with metric inputs and subjective quality assessments as class labels. The labels are adjusted using fuzzy measures of the distances from each class center computed using robust multivariate medians
Keywords
learning (artificial intelligence); multilayer perceptrons; pattern classification; software development management; software engineering; software metrics; software quality; fuzzy set theory; learning; median-adjusted class labels; multilayer perceptron; pattern classification; quality assessments; software development; software metrics; software quality prediction; training set; Multilayer perceptrons; Neural networks; Project management; Quality assessment; Quality management; Robustness; Software measurement; Software metrics; Software quality; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location
Honolulu, HI
ISSN
1098-7576
Print_ISBN
0-7803-7278-6
Type
conf
DOI
10.1109/IJCNN.2002.1007518
Filename
1007518
Link To Document