  Modern open source software development heavily relies on the issue tracking
systems to manage their feature requests, bug reports, tasks, and other similar
artifacts. Together, those "issues" form a complex network with links to each
other. The heterogeneous character of issues inherently results in varied link
types and therefore poses a great challenge for users to create and maintain
the label of the link manually. The goal of most existing automated issue link
construction techniques ceases with only examining the existence of links
between issues. In this work, we focus on the next important question of
whether we can assess the type of issue link automatically through a
data-driven method. We analyze the links between issues and their labels used
the issue tracking system for 66 open source projects. Using three projects, we
demonstrate promising results when using supervised machine learning
classification for the task of link label recovery with careful model selection
and tuning, achieving F1 scores of between 0.56-0.70 for the three studied
projects. Further, the performance of our method for future link label
prediction is convincing when there is sufficient historical data. Our work
signifies the first step in systematically manage and maintain issue links
faced in practice.
