Software Fault Prediction

Akalya Devi C.

ISBN 10: 3659144819 / ISBN 13: 9783659144813

Published by LAP LAMBERT Academic Publishing Jun 2012, 2012

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -Quality of the software is an important factor for any software company. Software fault prediction is a data mining process that helps to improve the quality. Data mining tools both open source and proprietary are available today. These bring lots of research works in this area. Software fault is the bug in the software that is identified only after its installation and it makes the software behave not in the expected way. Bug is there even after testing due to various constraints like cost, time. Prediction will help identify those fault prone areas and with that one can concentrate on those modules in future. Hybrid Feature Selection and Hybrid Classifier approach is a way to improve the software fault prediction accuracy. In Hybrid feature selection, irrelevant, redundant features are first filtered and this filtered feature set reduces the input feature set of wrapper. In Hybrid Classifier approach Linear Discriminant Analysis score is used as an additional feature for Neural Network classifier. These models give a better fault prediction accuracy. 72 pp. Englisch.

Seller Inventory # 9783659144813

Bibliographic Details

• Title: Software Fault Prediction
• Publisher: LAP LAMBERT Academic Publishing Jun 2012
• Publication Date: 2012
• Language: English
• ISBN 10: 3659144819
• ISBN 13: 9783659144813
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 5D millimeters 220 millimeters width by 150 millimeters height by 5 millimeters depth
• Weight: 125 grams

About this title

Synopsis

Quality of the software is an important factor for any software company. Software fault prediction is a data mining process that helps to improve the quality. Data mining tools both open source and proprietary are available today. These bring lots of research works in this area. Software fault is the bug in the software that is identified only after its installation and it makes the software behave not in the expected way. Bug is there even after testing due to various constraints like cost, time. Prediction will help identify those fault prone areas and with that one can concentrate on those modules in future. Hybrid Feature Selection and Hybrid Classifier approach is a way to improve the software fault prediction accuracy. In Hybrid feature selection, irrelevant, redundant features are first filtered and this filtered feature set reduces the input feature set of wrapper. In Hybrid Classifier approach Linear Discriminant Analysis score is used as an additional feature for Neural Network classifier. These models give a better fault prediction accuracy.

About the Author

C. Akalya devi is an M.E student in Sri Shakthi Institute of Engineering and Technology, Affiliated to Anna University Coimbatore, Tamil Nadu, India. She did her B.E in Information Technology in 2002 and was a lecturer. Her research interest includes software quality prediction and web and data mining.