Impact of AI-Driven Techniques in Software Impact Analysis
TL;DR
RUSBoost- based model is more effective than the past models of defect prediction on EQ data with an AUC of 0.946 and XGBoost classifier obtains the high performance on the PDE dataset, which is better than the classical and neural network baselines.
Abstract
Software defect prediction (SDP) becomes extremely important in enhancing the quality of software. Recent progresses in machine learning and ensemble learning have led to a great improvement on the prediction model of defects. In this research, a test defect prediction model combining state of (XGBoost, LightGBM, CatBoost) and balanced ensemble (EasyEnsemble, RUSBoost, Balanced Random Forest). The model is assessed on 5 standard AEEEM benchmark problems (EQ, JDT, LC, ML, PDE) with SMOTE oversampling and on the hold-out test strategy. The results of the experiment show RUSBoost- based model is more effective than the past models of defect prediction on EQ data with an AUC of 0.946, CatBoost model has an AUC of 0.850 on the JDT dataset, XGBoost method that uses on the LC sample has an AUC of 0.782, which is better than classical and other machine-learning-based methods published before, RUSBoost model is superior to the traditional and deep-learning-based models, which have the largest AUC of 0.757 to date on the ML dataset, XGBoost classifier obtains the high performance on the PDE dataset, with an AUC of 0.816, which is better than the classical and neural network baselines.