Developing a defect free software system is very difficult and most of the time there are some unknown bugs or unforeseen deficiencies even in software projects where the principles of the software development methodologies were applied carefully. Due to some defective software modules, the maintenance phase of software projects could become really painful for the users and costly for the enterprises. In previous work, original data was taken with 21 features and 21 features are having high dimension features which increase the complexity of processing. Ignored the boundary decision for software default predictor because boundary condition is not detected by the previously used classifier. Features of compaction were not considered because of that information is overlapped and the prediction error is increased. They are not able to train the component based classifier which results in more prediction error.