  An issue documents discussions around required changes in issue-tracking
systems, while a commit contains the change itself in the version control
systems. Recovering links between issues and commits can facilitate many
software evolution tasks such as bug localization, and software documentation.
A previous study on over half a million issues from GitHub reports only about
42.2% of issues are manually linked by developers to their pertinent commits.
Automating the linking of commit-issue pairs can contribute to the improvement
of the said tasks. By far, current state-of-the-art approaches for automated
commit-issue linking suffer from low precision, leading to unreliable results,
sometimes to the point that imposes human supervision on the predicted links.
The low performance gets even more severe when there is a lack of textual
information in either commits or issues. Current approaches are also proven
computationally expensive.
  We propose Hybrid-Linker to overcome such limitations by exploiting two
information channels; (1) a non-textual-based component that operates on
non-textual, automatically recorded information of the commit-issue pairs to
predict a link, and (2) a textual-based one which does the same using textual
information of the commit-issue pairs. Then, combining the results from the two
classifiers, Hybrid-Linker makes the final prediction. Thus, every time one
component falls short in predicting a link, the other component fills the gap
and improves the results. We evaluate Hybrid-Linker against competing
approaches, namely FRLink and DeepLink on a dataset of 12 projects.
Hybrid-Linker achieves 90.1%, 87.8%, and 88.9% based on recall, precision, and
F-measure, respectively. It also outperforms FRLink and DeepLink by 31.3%, and
41.3%, regarding the F-measure. Moreover, Hybrid-Linker exhibits extensive
improvements in terms of performance as well.
