Comparative Analysis of Supervised Learning Techniques of Machine Learning for Software Defect Prediction

Software Bug prediction or defect prediction is very important for the organizations to detect the bugs in the early stage of Software development process because software developers can know the vulnerable areas where the defects may be present.In this research paper we have compared different statistical techniques like Linear Regression, Naïve Bayes, Random Forest, Decision Tree, Artificial Neural Networks etc. and come up with the best among them for the Bug prediction. Comparison is made using Performance Measures like Accuracy, precision, recall and F-measure.