Software Fault Prediction Using Canonical Discriminant Quadratic Regressive Milboost Ensemble classifier


  • T. Shathish Kumar, B. Booba


Software fault prediction, Jarque–Bera stochastic test-based metric dispersal class extractions, generalized canonical correlative normal discriminant analysis based software metrics selection, Iterative Dichotomize Linear Regressive Quadratic MilBoost.


The aim of software fault prediction is to sense fault-prone software modules and enhances software quality as well as testing effectiveness through early recognition of faults. It aids to achieve desired software quality through lower cost. Earlier fault prediction classification algorithms to forecast fault-prone software modules. The prediction accuracy of conventional techniques was established to be significantly minimized with better misclassification. The selection of significant metrics from the source code is the fundamental step in the software prediction process. Therefore a novel technique called CAnonical Discriminant Quadratic Regressive Milboost Ensemble (CADME) technique is introduced for improving the software prediction accuracy as well as minimizing misclassification rate. Proposed CADME technique comprised metric selection , classification. Initially, number of JAVA packages given as input from the dataset. Then the metric dispersal class extractions are carried by using Jarque–Bera stochastic test. After the class extraction, the important software metrics are chosen for software prediction using generalized canonical correlative normal discriminant analysis. Following the metric selection, the software fault prediction is through by means of Iterative Dichotomize Linear Regressive Quadratic MilBoost. As a result of JAVA classes known with defects or not are predicted in an accurate manner by reducing the loss. This assists to develop the precision and F-score in fault prediction. Simulation results are performed on factors namely accuracy, precision, recall, F-score, and time complexity. The results as well as discussion of various metrics specified that proposed CADME technique improves the accuracy and minimum time complexity of software prediction than the conventional methods.


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How to Cite

T. Shathish Kumar,. (2024). Software Fault Prediction Using Canonical Discriminant Quadratic Regressive Milboost Ensemble classifier. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 3244–3258. Retrieved from



Research Article