A Systematic Approach Towards Development of Universal Software Fault Prediction Model Using Object-Oriented Design Measurement

2018 ◽  
pp. 515-526
Author(s):  
Dipti Kumari ◽  
Kumar Rajnish
Keyword(s):  
Prediction Model ◽  
Systematic Approach ◽  
Object Oriented ◽  
Fault Prediction ◽  
Software Fault Prediction ◽  
Object Oriented Design ◽  
Software Fault

Object-oriented software fault prediction using neural networks

2007 ◽  
Vol 49 (5) ◽  
pp. 483-492 ◽  
Author(s):  
S. Kanmani ◽  
V. Rhymend Uthariaraj ◽  
V. Sankaranarayanan ◽  
P. Thambidurai
Keyword(s):  
Neural Networks ◽  
Object Oriented ◽  
Fault Prediction ◽  
Software Fault Prediction ◽  
Software Fault

scholarly journals Statistical and Machine Learning Methods for Software Fault Prediction Using CK Metric Suite: A Comparative Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Yeresime Suresh ◽  
Lov Kumar ◽  
Santanu Ku. Rath
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Logistic Regression ◽  
Linear Regression ◽  
Object Oriented ◽  
Fault Prediction ◽  
Learning Methods ◽  
Software Fault Prediction ◽  
Machine Learning Methods ◽  
Software Fault

Experimental validation of software metrics in fault prediction for object-oriented methods using statistical and machine learning methods is necessary. By the process of validation the quality of software product in a software organization is ensured. Object-oriented metrics play a crucial role in predicting faults. This paper examines the application of linear regression, logistic regression, and artificial neural network methods for software fault prediction using Chidamber and Kemerer (CK) metrics. Here, fault is considered as dependent variable and CK metric suite as independent variables. Statistical methods such as linear regression, logistic regression, and machine learning methods such as neural network (and its different forms) are being applied for detecting faults associated with the classes. The comparison approach was applied for a case study, that is, Apache integration framework (AIF) version 1.6. The analysis highlights the significance of weighted method per class (WMC) metric for fault classification, and also the analysis shows that the hybrid approach of radial basis function network obtained better fault prediction rate when compared with other three neural network models.

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