DOI: 10.4018/IJAMC.2019010103 International Journal of Applied Metaheuristic Computing Volume 10 • Issue 1 • January-March 2019 Copyright © 2019, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 43 Cross-Project Change Prediction Using Meta-Heuristic Techniques Ankita Bansal, Netaji Subhas Institute of Technology, Delhi, India Sourabh Jajoria, Netaji Subhas Institute of Technology, Delhi, India ABSTRACT Changes in software systems are inevitable. Identification of change-prone modules can help developers to focus efforts and resources on them. In this article, the authors conduct various intra-project and cross-project change predictions. The authors use distributional characteristics of dataset to generate rules which can be used for successful change prediction. The authors analyze the effectiveness of meta-heuristic decision trees in generating rules for successful cross-project change prediction. The employed meta-heuristic algorithms are hybrid decision tree genetic algorithms and oblique decision trees with evolutionary learning. The authors compare the performance of these meta-heuristic algorithms with C4.5 decision tree model. The authors observe that the accuracy of C4.5 decision tree is 73.33%, whereas the accuracy of the hybrid decision tree genetic algorithm and oblique decision tree are 75.00% and 75.56%, respectively. These values indicate that distributional characteristics are helpful in identifying suitable training set for cross-project change prediction. KeywoRdS Change-Prone, Cross-Validation, Decision Tree, Distribution Characteristics, Ensemble Learners, Evolutionary Algorithms, Open Source, Receiver Operating Characteristics 1. INTRodUCTIoN Change-proneness is the likelihood that a particular class will be changed in future versions of software (Tsantalis, 2005). Due to the increasing size, complexity, changing demands of customer etc., the changes in software are unavoidable. Incorporating these changes is essential but at the same time, requires huge amount of resources. Additionally, changes in one class may also further lead to changes in the other classes. The largest percentage of the software development effort is spent on rework and maintenance (Brooks, 1974). Due the availability of limited resources, it is useful to identify some classes which may be more prone to changes in future as compared to the other classes. During the initial phases of software development life cycle, we identify the change prone classes with the help of software metrics. Identification of such classes will allow the developers and testers to pay focused attention on them, thus, leading to judicious allocation of limited resources in terms of time, money and manpower. Building a prediction model requires training data. In case of change prediction, the training data is the large amount of historical data of the project. Intra-project change prediction models use historical data of the same project to predict change-prone classes. But sometimes, the historical data might not be available like in the case of first version of software. Cross-project change prediction refers to predicting change-prone classes from training data of other projects. It is important to find