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Cross-Project Change Prediction Using Meta-Heuristic Techniques

Cross-Project Change Prediction Using Meta-Heuristic Techniques

Ankita Bansal, Sourabh Jajoria
Copyright: © 2019 |Volume: 10 |Issue: 1 |Pages: 19
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781522566069|DOI: 10.4018/IJAMC.2019010103
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MLA

Bansal, Ankita, and Sourabh Jajoria. "Cross-Project Change Prediction Using Meta-Heuristic Techniques." IJAMC vol.10, no.1 2019: pp.43-61. http://doi.org/10.4018/IJAMC.2019010103

APA

Bansal, A. & Jajoria, S. (2019). Cross-Project Change Prediction Using Meta-Heuristic Techniques. International Journal of Applied Metaheuristic Computing (IJAMC), 10(1), 43-61. http://doi.org/10.4018/IJAMC.2019010103

Chicago

Bansal, Ankita, and Sourabh Jajoria. "Cross-Project Change Prediction Using Meta-Heuristic Techniques," International Journal of Applied Metaheuristic Computing (IJAMC) 10, no.1: 43-61. http://doi.org/10.4018/IJAMC.2019010103

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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.

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