  Machine Learning approaches are good in solving problems that have less
information. In most cases, the software domain problems characterize as a
process of learning that depend on the various circumstances and changes
accordingly. A predictive model is constructed by using machine learning
approaches and classified them into defective and non-defective modules.
Machine learning techniques help developers to retrieve useful information
after the classification and enable them to analyse data from different
perspectives. Machine learning techniques are proven to be useful in terms of
software bug prediction. This study used public available data sets of software
modules and provides comparative performance analysis of different machine
learning techniques for software bug prediction. Results showed most of the
machine learning methods performed well on software bug datasets.
