By Topic

Software Defect Identification Using Machine Learning Techniques

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Evren Ceylan ; Bogazi¿i University, Turkey ; F. Onur Kutlubay ; Ayse B. Bener

Software engineering is a tedious job that includes people, tight deadlines and limited budgets. Delivering what customer wants involves minimizing the defects in the programs. Hence, it is important to establish quality measures early on in the project life cycle. The main objective of this research is to analyze problems in software code and propose a model that will help catching those problems earlier in the project life cycle. Our proposed model uses machine learning methods. Principal component analysis is used for dimensionality reduction, and decision tree, multi layer perceptron and radial basis functions are used for defect prediction. The experiments in this research are carried out with different software metric datasets that are obtained from real-life projects of three big software companies in Turkey. We can say that, the improved method that we proposed brings out satisfactory results in terms of defect prediction

Published in:

32nd EUROMICRO Conference on Software Engineering and Advanced Applications (EUROMICRO'06)

Date of Conference:

Aug. 29 2006-Sept. 1 2006