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• W2894967499 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." @default.
• W2894967499 created "2018-10-12" @default.
• W2894967499 creator A5040620149 @default.
• W2894967499 creator A5071433796 @default.
• W2894967499 date "2019-01-01" @default.
• W2894967499 modified "2023-09-27" @default.
• W2894967499 title "Cross-Project Change Prediction Using Meta-Heuristic Techniques" @default.
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• W2894967499 doi "https://doi.org/10.4018/ijamc.2019010103" @default.
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