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Comparison of Resilient Backpropagation ＆ Fuzzy Clustering Based Approach for Prediction of Level of Severity of Faults in Software Systems

Comparison of Resilient Backpropagation ＆ Fuzzy Clustering ...

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International Conference on Intelligent Computational Systems

作者： Kritika Gupta sandeep kang Parvinder S.Sandhu Chandigarh Engineering College CSE Deptt. CEC (Chandigarh Engineering College) RayatBahra Institute of Engineering and Bio-Technology

There is lot of work done in prediction of the fault proneness of the software systems. But, it is the severity of the faults that is more important than number of faults existing in the developed system as the major faults matters most for a developer and those major faults needs immediate attention. As, Neural networks, which have been already applied in software engineering applications to build reliability growth models predict the gross change or reusability metrics. In which majority of faults are found in a few of its modules so there is a need to investigate the modules that are affected severely as compared to other modules and proper maintenance need to be done in time especially for the critical applications. In this paper, Resilient Backpropagation based Neural Network ＆ fuzzy clustering Based techniques are discussed and comparative analysis is performed in order to predict level of impact of faults in NASA’s public domain defect dataset. Predicting faults in the software life cycle can be used to improve software process control and achieve high software reliability. The results show that when the best prediction techniques are evaluated.

关键词： Neural Network Software Faults Software Metric Fuzzy Clustering Software Quality

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