Abstract

Aim of study software defect is a flaw, miscalculation, or failure, in a computer program or framework delivering an inappropriate or surprising result, or making it perform in an unintended way. Software Defect Prediction (SDP) finds defective modules in software. The final product ought to have as few defects as possible to create top notch software. Early software defects discovery prompts diminished development costs and rework effort and better software. Software metrics guarantee quantitative methods to survey software quality. Software metrics are helpful to software process and product metrics. Thus, a defect prediction study is critical to guarantee quality software and software metric aggregation. In this study, the efficiency of classifier for SDP is assessed. Diverse classifiers like Naïve Bayes, K Nearest Neighbor (KNN), C4.5 and Multilayer Perceptrons Neural Network (MLPNN) are assessed for SDP.

Keywords:

C4.5 and Multilayer Perceptrons Neural Network (MLPNN), K Nearest Neighbor (KNN) , Na, Software Defect Prediction (SDP) , software metric,

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Competing interests

The authors have no competing interests.

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