Software Defect Prediction Using Machine Learning Techniques

Abstract

Software defect prediction gives development teams observable results while influencing business outcomes and development flaws. Developers can uncover flaws and plan test activities by anticipating problematic code sections. Early identification depends on the percentage of classifications that make the right prediction. Additionally, software-defective data sets are supported and partially acknowledged because of their vast size. The confusion, precision, recall, identification accuracy, etc., are assessed and compared with the existing schemes in a systematic research analysis. Previous research has employed the weak simulation tool for software analysis, but this study proposes building three machine learning models using linear regression, KNN classifier, and random forest (RF). According to the analytical investigation, the suggested approach will offer more beneficial options for predicting device failures. Moreover, software-defected data sets are supported and at least partially recognized due to their enormous dimension.