Software fault prediction using BP-based crisp artificial neural networks

被引：3

作者：

Abaei, Golnoush ^{[1
]}

Mashinchi, M. Reza ^{[1
]}

Selamat, Ali ^{[1
]}

机构：

[1] UTM-IRDA Center of Excellence UTM, Faculty of Computing, Universiti Teknologi Malaysia, UTM Johor Bahru, Johor

来源：

International Journal of Intelligent Information and Database Systems | 2015年 / 9卷 / 01期

关键词：

Accuracy; CANNs; CFS; Class-level metrics; Correlation-based feature selection; Crisp artificial neural networks; Dimensionality reduction; F-measure; fault prediction; PCA; Precision; Principle component analysis; Recall;

D O I：

10.1504/IJIIDS.2015.070825

中图分类号：

学科分类号：

摘要：

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. © 2015 Inderscience Enterprises Ltd.

引用

页码：15 / 31

页数：16

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