Predicting Licenses in Changed Source Code

Xiaoyu Liu, LiGuo Huang, Jidong Ge, and Vincent Ng.
Proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering, pp. 686-697, 2019.

Click here for the PDF version. The talk slides are available here.


Open source software licenses regulate the circumstances under which software can be redistributed, reused and modified. Ensuring license compatibility and preventing license restriction conflicts among source code during software changes are the key to protect their commercial use. However, selecting the appropriate licenses for software changes requires lots of experience and manual effort that involve examining, assimilating and comparing various licenses as well as understanding their relationships with software changes. Worse still, there is no state-of-the-art methodology to provide this capability. Motivated by this observation, we propose in this paper Automatic License Prediction (ALP), a novel learning-based method and tool for predicting licenses as software changes. An extensive evaluation of ALP on predicting licenses in 700 open source projects demonstrate its effectiveness: ALP can achieve not only a high overall prediction accuracy (92.5% in micro F1 score) but also high accuracies across all license types.

BibTeX entry

  author = {Xiaoyu Liu and LiGuo Huang and Jidong Ge Vincent Ng},
  title = {Predicting Licenses in Changed Source Code},
  booktitle = {Proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering},
  pages = {686--697}, 
  year = 2019}