Software failure prediction is the process of building models that software interpreters can use to detect faulty constructs early in the software development life cycle. Faults are the main source of time consumption and cost less over the life cycle of applications. Early failure prediction increases device consistency and reliability and decreases the expense of software development. However, machine learning techniques are also valuable in detecting software bugs. There are various machine learning techniques for finding bugs, ambiguities, and faulty software. In this paper, we direct an exploratory review to assess the performance of popular techniques including logistical regression, decision tree, random forest algorithm, SVM algorithms, and DNN. Our experiment is performed on various types of datasets (jedit, Tomcat, Tomcat-1, Xalan, Xerces, and prop-6). The experimental results show that DNN produces a better accuracy among all techniques used above.