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

Application of Artificial Immune Systems Paradigm for Developing Software Fault Prediction Models

2012 ◽  
pp. 371-387 ◽  
Author(s):  
Cagatay Catal ◽  
Soumya Banerjee
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Software Metrics ◽  
Prediction Models ◽  
Artificial Immune Systems ◽  
Fault Prediction ◽  
Artificial Immune ◽  
Software Fault Prediction ◽  
Immune Systems ◽  
Software Fault

Artificial Immune Systems, a biologically inspired computing paradigm such as Artificial Neural Networks, Genetic Algorithms, and Swarm Intelligence, embody the principles and advantages of vertebrate immune systems. It has been applied to solve several complex problems in different areas such as data mining, computer security, robotics, aircraft control, scheduling, optimization, and pattern recognition. There is an increasing interest in the use of this paradigm and they are widely used in conjunction with other methods such as Artificial Neural Networks, Swarm Intelligence and Fuzzy Logic. In this chapter, we demonstrate the procedure for applying this paradigm and bio-inspired algorithm for developing software fault prediction models. The fault prediction unit is to identify the modules, which are likely to contain the faults at the next release in a large software system. Software metrics and fault data belonging to a previous software version are used to build the model. Fault-prone modules of the next release are predicted by using this model and current software metrics. From machine learning perspective, this type of modeling approach is called supervised learning. A sample fault dataset is used to show the elaborated approach of working of Artificial Immune Recognition Systems (AIRS).

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.

Application of Artificial Immune Systems Paradigm for Developing Software Fault Prediction Models

2010 ◽  
pp. 76-93 ◽  
Author(s):  
Cagatay Catal ◽  
Soumya Banerjee
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Software Metrics ◽  
Prediction Models ◽  
Artificial Immune Systems ◽  
Fault Prediction ◽  
Artificial Immune ◽  
Software Fault Prediction ◽  
Immune Systems ◽  
Software Fault

Artificial Immune Systems, a biologically inspired computing paradigm such as Artificial Neural Networks, Genetic Algorithms, and Swarm Intelligence, embody the principles and advantages of vertebrate immune systems. It has been applied to solve several complex problems in different areas such as data mining, computer security, robotics, aircraft control, scheduling, optimization, and pattern recognition. There is an increasing interest in the use of this paradigm and they are widely used in conjunction with other methods such as Artificial Neural Networks, Swarm Intelligence and Fuzzy Logic. In this chapter, we demonstrate the procedure for applying this paradigm and bio-inspired algorithm for developing software fault prediction models. The fault prediction unit is to identify the modules, which are likely to contain the faults at the next release in a large software system. Software metrics and fault data belonging to a previous software version are used to build the model. Fault-prone modules of the next release are predicted by using this model and current software metrics. From machine learning perspective, this type of modeling approach is called supervised learning. A sample fault dataset is used to show the elaborated approach of working of Artificial Immune Recognition Systems (AIRS).

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