A Conceptual Framework for Software Fault Prediction Using Neural Networks

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).

Download Full-text

Object-oriented software fault prediction using neural networks

Information and Software Technology ◽

10.1016/j.infsof.2006.07.005 ◽

2007 ◽

Vol 49 (5) ◽

pp. 483-492 ◽

Cited By ~ 100

Author(s):

S. Kanmani ◽

V. Rhymend Uthariaraj ◽

V. Sankaranarayanan ◽

P. Thambidurai

Keyword(s):

Neural Networks ◽

Object Oriented ◽

Fault Prediction ◽

Software Fault Prediction ◽

Software Fault

Download Full-text

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

Evolutionary Computation and Optimization Algorithms in Software Engineering ◽

10.4018/978-1-61520-809-8.ch005 ◽

2010 ◽

pp. 76-93 ◽

Cited By ~ 1

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).

Download Full-text

Software fault prediction using BP-based crisp artificial neural networks

International Journal of Intelligent Information and Database Systems ◽

10.1504/ijiids.2015.070825 ◽

2015 ◽

Vol 9 (1) ◽

pp. 15

Author(s):

Golnoush Abaei ◽

M. Reza Mashinchi ◽

Ali Selamat

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Fault Prediction ◽

Software Fault Prediction ◽

Software Fault ◽

Artificial Neural

Download Full-text

The Effectiveness of the Fused Weighted Filter Feature Selection Method to Improve Software Fault Prediction

Journal of Communications Technology Electronics and Computer Science ◽

10.22385/jctecs.v8i0.96 ◽

2016 ◽

Vol 8 ◽

pp. 5 ◽

Cited By ~ 1

Author(s):

Fatemeh Alighardashi ◽

Mohammad Ali Zare Chahooki

Keyword(s):

Feature Selection ◽

Feature Selection Method ◽

Selection Method ◽

Machine Learning Algorithms ◽

Fault Prediction ◽

Filter Method ◽

Selection Methods ◽

Software Projects ◽

Software Fault Prediction ◽

Software Fault

Improving the software product quality before releasing by periodic tests is one of the most expensive activities in software projects. Due to limited resources to modules test in software projects, it is important to identify fault-prone modules and use the test sources for fault prediction in these modules. Software fault predictors based on machine learning algorithms, are effective tools for identifying fault-prone modules. Extensive studies are being done in this field to find the connection between features of software modules, and their fault-prone. Some of features in predictive algorithms are ineffective and reduce the accuracy of prediction process. So, feature selection methods to increase performance of prediction models in fault-prone modules are widely used. In this study, we proposed a feature selection method for effective selection of features, by using combination of filter feature selection methods. In the proposed filter method, the combination of several filter feature selection methods presented as fused weighed filter method. Then, the proposed method caused convergence rate of feature selection as well as the accuracy improvement. The obtained results on NASA and PROMISE with ten datasets, indicates the effectiveness of proposed method in improvement of accuracy and convergence of software fault prediction.

Download Full-text