Lit review on transfer learning

Transfer Learning for Software Engineering:
A Literature Review

Tim Menzies, Fayola Peters 
Lane Department of CS & EE, WVU, USA

Forrest Shull, Lucas Layman
Fraunhofer Center for Experimental SE, College Park, MD, USA

tim@menzies.us,
fayolapeters@gmail.com {
fshull,llayman}@fc-md.umd.edu

For decades, empirical methods in SE have focused on a mostly manual analysis of project data. That approach has often suffered from lack of transfer since it was hard to migrate lessons learned from one project to another.

Recently, there has been much success with automatic transfer learning between SE projects. This short paper reviews that work to observe that (1) transfer learning has been far more successful at transferring lessons learned than traditional SE methods; (2) past research on transfer learning has uncovered numerous issues and open issues that need exploring.

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