Devi C. Akalya, B. Surendiran, K. E. Kannammal

Software Fault Prediction

A Software Fault Prediction Model by Hybrid Feature Selection and Hybrid Classifier Approach

• Broschiertes Buch

Produktbeschreibung

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 NeuralNetwork classifier. These models give a better fault prediction accuracy.

Produktdetails

• Verlag: LAP Lambert Academic Publishing
• Seitenzahl: 72
• Erscheinungstermin: 13. Juni 2012
• Englisch
• Abmessung: 220mm x 150mm x 5mm
• Gewicht: 125g
• ISBN-13: 9783659144813
• ISBN-10: 3659144819
• Artikelnr.: 36000341

Autorenporträt

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.