Cross-Project Change Prediction Using Meta-Heuristic Techniques
| Autor: | Ankita Bansal, Sourabh Jajoria |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
Control and Optimization Computer science Change prediction business.industry Machine learning computer.software_genre Computer Science Applications Computational Mathematics Computational Theory and Mathematics Modeling and Simulation Meta heuristic Decision Sciences (miscellaneous) Artificial intelligence business Cross project computer |
| Zdroj: | International Journal of Applied Metaheuristic Computing. 10:43-61 |
| ISSN: | 1947-8291 1947-8283 |
| DOI: | 10.4018/ijamc.2019010103 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|