Software Fault Prediction based on CURE Clustering Algorithm and Artificial Intelligence
Margi Patel, Upama Vachhani
CURE Clustering, Neural Network, Fault Prediction
There has been rapid growth of software development. During Transmission of Data faults are Created. However software Fault Prediction Techniques are used to Detect Fault. Software Fault Prediction improve the quality and reliability of software by predicting faults .Quality of Software measure in term of fault proneness of data .These software defect may lead to degradation of the quality which might be the cause of failure. We show a comparatively analysis of software fault prediction based on clustering technique, neural network method, statistical method. Fault prediction reduce the overall time and less data processing. In this paper, hybrid approach based on CURE clustering and Neural Network based approach has been performed with the real time data set named PC1 taken from NASA MDP software projects. The Performance is recorded on the basis of accuracy, MAE, RMSE values. This paper focus on clustering with large dataset and predicting faults efficiently.
Article Details
Unique Paper ID: 143599

Publication Volume & Issue: Volume 2, Issue 12

Page(s): 69 - 77
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