An ANN Based Approach for Software Fault Prediction Using Object Oriented Metrics

2018 ◽  
pp. 341-354
Author(s):  
Rajdeep Kaur ◽  
Sumit Sharma
Keyword(s):  
Object Oriented ◽  
Fault Prediction ◽  
Software Fault Prediction ◽  
Software Fault ◽  
Object Oriented Metrics

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.

scholarly journals Exclusive use and evaluation of inheritance metrics viability in software fault prediction—an experimental study

2021 ◽  
Vol 7 ◽  
pp. e563
Author(s):  
Syed Rashid Aziz ◽  
Tamim Ahmed Khan ◽  
Aamer Nadeem
Keyword(s):  
Software Metrics ◽  
Model Building ◽  
Object Oriented ◽  
Fault Prediction ◽  
Cross Entropy ◽  
Support Vector ◽  
Data Sets ◽  
Software Fault Prediction ◽  
Software Fault ◽  
Class Labels

Software Fault Prediction (SFP) assists in the identification of faulty classes, and software metrics provide us with a mechanism for this purpose. Besides others, metrics addressing inheritance in Object-Oriented (OO) are important as these measure depth, hierarchy, width, and overriding complexity of the software. In this paper, we evaluated the exclusive use, and viability of inheritance metrics in SFP through experiments. We perform a survey of inheritance metrics whose data sets are publicly available, and collected about 40 data sets having inheritance metrics. We cleaned, and filtered them, and captured nine inheritance metrics. After preprocessing, we divided selected data sets into all possible combinations of inheritance metrics, and then we merged similar metrics. We then formed 67 data sets containing only inheritance metrics that have nominal binary class labels. We performed a model building, and validation for Support Vector Machine(SVM). Results of Cross-Entropy, Accuracy, F-Measure, and AUC advocate viability of inheritance metrics in software fault prediction. Furthermore, ic, noc, and dit metrics are helpful in reduction of error entropy rate over the rest of the 67 feature sets.

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