Refactoring detection across languages: Leveraging Java-trained models for detecting class-level refactorings in Kotlin
Published in 51st Euromicro Conference on Software Engineering and Advanced Applications (SEAA 2025), Lecture Notes in Computer Science, vol. 16081, pp. 46–63, 2025
Recommended citation: M. M. Afkhami, I. Hemati Moghadam, V. Zaytsev, M. H. Ashoori, and H. Bazmandegan, ‘Refactoring detection across languages: Leveraging Java-trained models for detecting class-level refactorings in Kotlin,’ in SEAA 2025, Lecture Notes in Computer Science, vol. 16081, pp. 46–63, Springer, 2025. https://doi.org/10.1007/978-3-032-04190-6_4
This study evaluates zero-shot cross-language refactoring detection: Random Forest models trained on Java are applied to Kotlin projects and assessed against RefDetect annotations.
Authors: Mohammad Mehdi Afkhami, Iman Hemati Moghadam, Vadim Zaytsev, Mohammad Hosein Ashoori, Hossein Bazmandegan.
DOI: 10.1007/978-3-032-04190-6_4
