1473 Copyright © 2017, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 59 DOI: 10.4018/978-1-5225-1759-7.ch059 ABSTRACT The past 10 years have seen the prediction of software defects proposed by many researchers using vari- ous metrics based on measurable aspects of source code entities (e.g. methods, classes, fles or modules) and the social structure of software project in an efort to predict the software defects. However, these metrics could not predict very high accuracies in terms of sensitivity, specifcity and accuracy. In this chapter, we propose the use of machine learning techniques to predict software defects. The efectiveness of all these techniques is demonstrated on ten datasets taken from literature. Based on an experiment, it is observed that PNN outperformed all other techniques in terms of accuracy and sensitivity in all the software defects datasets followed by CART and Group Method of data handling. We also performed feature selection by t-statistics based approach for selecting feature subsets across diferent folds for a given technique and followed by the feature subset selection. By taking the most important variables, we invoked the classifers again and observed that PNN outperformed other classifers in terms of sensitiv- ity and accuracy. Moreover, the set of ‘if- then rules yielded by J48 and CART can be used as an expert system for prediction of software defects. INTRODUCTION Machine learning techniques have been dominating in the last two decades. The recently published comprehensive state-of-the-art review (Mohanty et al., 2010) justifies this issue. The ability of software quality models to accurately identify critical faulty components allows for the application of focused verification activities ranging from manual inspection to automated formal analysis methods. Therefore, software quality models to ensure the reliability of the delivered products. Accurate prediction of fault prone modules enables the verification and validation activities that includes quality models: Musa, Machine Learning Techniques to Predict Software Defect Ramakanta Mohanty Keshav Memorial Institute of Technology, India Vadlamani Ravi Institute for Development and Research in Banking Technology (IDRBT), India