Cross-Project Change Prediction Using Meta-Heuristic Techniques

Ankita Bansal and Sourabh Jajoria
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Ankita Bansal: Netaji Subhas Institute of Technology, Delhi, India
Sourabh Jajoria: Netaji Subhas Institute of Technology, Delhi, India

International Journal of Applied Metaheuristic Computing (IJAMC), 2019, vol. 10, issue 1, 43-61

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.

Date: 2019
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