Abstract Software maintenance deals with changes or modifications which software goes through. Change prediction models help in identification of classes/modules which are prone to change in future releases of a software product. As change prone classes are probable sources of defects and modifications, they represent the weak areas of a product. Thus, change prediction models would aid software developers in delivering an effective quality software product by allocating more resources to change prone classes/modules as they need greater attention and resources for verification and meticulous testing. This would reduce the probability of defects in future releases and would yield a better quality product and satisfied customers. This study deals with the identification of change prone classes in an Object-Oriented (OO) software in order to evaluate whether a relationship exists between OO metrics and change proneness attribute of a class. The study also compares the effectiveness of two sets of methods for change prediction tasks i.e. the traditional statistical methods (logistic regression) and the recently widely used machine learning methods like Bagging, Multi-layer perceptron etc.

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[15] Zhou Y., Leung H., and Xu B., Examining the Potentially Confounding Effect of Class Size on the Associations Between Object-Oriented Metrics and Change Proneness, IEEE Transactions on Software Engineering, vol. 35, no. 5, pp. 607-623, 2009. Ruchika Malhotra is Associate Head and Associate Professor in the Discipline of Software Engineering, Department of Computer Science & Engineering, Delhi Technological University (formerly Delhi College of Engineering), Delhi, India. She was awarded with prestigious Raman Fellowship for pursuing Post doctoral research in Indiana University Purdue University Indianapolis USA. She received her master's and doctorate degree in software engineering from the University School of Information Technology, Guru Gobind Singh Indraprastha University, Delhi, India. She was an Assistant Professor at the University School of Information Technology, Guru Gobind Singh Indraprastha University, Delhi, India. She has received IBM Faculty Award 2013. She is recipient of Commendable Research Award by Delhi Technological University. Her h-index is 24 as reported by Google Scholar. She is author of book titled Empirical Research in Software Engineering published by CRC press and co-author of a book on Object Oriented Software Engineering published by PHI Learning. Her research interests are in software testing, improving software quality, statistical and adaptive prediction models, software metrics and the definition and validation of software metrics. She has published more than 150 research papers in international journals and conferences. Megha Khanna is currently pursuing her doctoral degree from Delhi Technological University. She is currently working as Assistant Professor in Sri Guru Gobind Singh College of Commerce, University of Delhi. She completed her master s degree in software engineering in 2010 from the University School of Information Technology, Guru Gobind Singh Indraprastha University, India. She received her graduation degree in computer science (Hons.) in 2007 from Acharya Narendra Dev College, University of Delhi. Her research interests are in software quality improvement, applications of machine learning techniques in change prediction, and the definition and validation of software metrics. She has various publications in international conferences and journals.