Software Defect Prediction is an important aspect in order to ensure software quality. Deep Learning techniques can also be used for the same.
In this project we use Random forest, Convolutional Neural Networks, SVM, Decision Tree, Naive Bayes and Artificial Neural Network to train the model with the data. After getting different results from these techniques, we combine them through Logistic Regression and get the final output.
We use different open source datasets from NASA Promise Data Repository to perform this comparative study.
For evaluation, three widely used metrics: Accuracy, F1 scores and Areas under Receiver Operating Characteristic curve are used. It is found that Artificial Neural Network outperformed all the other dimensionality reduction techniques.