부산대학교 도서관

In modern software engineering practices, various kinds of automated and intelligent methodologies have been proposed to improve the efficiency of collaborative development. However, most of those approaches are heavily dependent on supervised or semi-supervised learning technologies, which would be restricted by the lack of training data. Inspired by the theories and techniques of transfer learning, cross-project approaches have been proposed, but hard to achieve a consistent and desirable performances. In this paper, we conduct an extensive empirical study to capture the determinants that affect the performances of transferring reusable models across projects in the context of issue classification. Starting from a large-scale dataset, containing 799 OSS projects and more than 795,000 issues, we have extracted 28 attributes grouped into 4 different dimensions. The results show that the performance of cross-project issue classification based on model transferring is sensitive and unstable, which is influenced by multiple factors spreading among transferred model training, project construction, and technical and socail relations between source and target.