Software Defect Prediction Using Ensemble Modelling and Class Balancing

Software product is a kind of software whose development is done to accomplish a precise requirement. Meanwhile, engineering is branch which is related to develop a product based on explicit technical fundamentals and techniques. There are diverse phases executed to predict the defect in software such as to employ the data for input, pre-process it, extract the attributes and classify the defect. This research work presents numerous algorithms, namely gaussian naive bayes (GNB), Bernoulli NB, random forest (RF) and multi-layer perceptron (MLP), for predicting the software defect. This work focuses on developing an ensemble algorithm to enhance the efficacy of predicting the defects. This ensemble consisted of Principal Component Analysis (PCA) algorithm with class balancing. Python is executed for simulating the introduced model. Diverse parameters such as accuracy, precision and recall are employed for analyzing the results.