A systematic review of transfer learning in software engineering

Nowadays, everyone requires a good quality software. The quality of software can’t be assured due to lack of data availability for training, and testing. Thus, Transfer Learning (TL) plays an important role in the reusability of existing software for developing new software with a similar domain and task. TL focused on transferring knowledge from existing prediction models for the development of new prediction models. The developed models are used for unseen datasets based on the characteristics, and nature of the dataset. The sufficient amount of training data is unavailable. The data distribution and task of the source and target project must be checked before employing TL for software development. In this Systematic Review (SR), we have investigated 39 studies from January 1990 to March 2024 that used TL in the software engineering domain. The review focused on the identification of Machine Learning (ML) techniques used with TL techniques, types of TL explored, TL settings explored, experimental setting, dataset, quality attribute, validation methods, threats to validity, strengths and weakness of TL techniques, and hybrid techniques with TL. According to the experimental comparison, the performance of TL techniques is encouraging. The findings of this SR paper will serve as guidelines for academicians, software industry experts, software developers, software testers, and researchers. This SR is also helpful in the selection of appropriate types of TL and TL settings for the development of efficient software in the future based on the type of problem and TL setting. Thus, this study showed that 30.67% of the studies are focused on defect prediction, that used 15% open-source dataset. Further, 35% of studies used SVM as a base classifier for TL, and different independent variables of the used dataset are considered as prediction model input. Further, the K-fold Cross-Validation (CV) method is used in 15 studies.