Software fault prediction using BP-based crisp artificial neural networks
by Golnoush Abaei; M. Reza Mashinchi; Ali Selamat
International Journal of Intelligent Information and Database Systems (IJIIDS), Vol. 9, No. 1, 2015

Abstract: Early fault detection for software reduces the cost of developments. Fault level can be predicted through learning mechanisms. Conventionally, precise metrics measure the fault level and crisp artificial neural networks (CANNs) perform the learning. However, the performance of CANNs depends on complexities of data and learning algorithm. This paper considers these two complexities to predict the fault level of software. We apply the principle component analysis (PCA) to reduce the dimensionality of data, and employ the correlation-based feature selection (CFS) to select the best features. CANNs, then, predict the fault level of software using back propagation (BP) algorithm as a learning mechanism. To investigate the performance of BP-based CANNs, we analyse varieties of dimensionality reduction. The results reveal the superiority of PCA to CFS in terms of accuracy.

Online publication date: Tue, 28-Jul-2015

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligent Information and Database Systems (IJIIDS):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com