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LLMs Based Data Augmentation Techniques for Python Code Refactoring
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DOI：10.1007/978-3-032-04190-6_2.png)
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Abstract
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Refactoring is a crucial software engineering practice aimed at improving code quality. However, detecting and predicting refactoring activities automatically remains a challenging task due to the limited availability of labeled datasets. This study investigates the role of data augmentation techniques in enhancing refactoring detection models. We apply various augmentation strategies to expand training data and assess their impact on model performance. We also discuss validation and balancing of the resulting dataset in order to provide meaningful data for further applied ML techniques. Our findings highlight the importance of data diversity in automated refactoring detection and provide insights into optimizing augmentation strategies for software engineering applications. Experimental results demonstrate that data augmentation improves the robustness and accuracy of refactoring detection models by mitigating overfitting and enhancing generalization.
Keywords:
Refactoring
Artificial Intelligence
AI in Software Refactoring
Machine Learning
Data Augmentation
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Cited Papers
ShareSaveSMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary
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OAAIShareSaveDetecting Refactoring Commits in Machine Learning Python Projects: A Machine Learning-Based ApproachOAAI
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Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
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Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data MiningIF0
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