  Software projects use Issue Tracking Systems (ITS) like JIRA to track issues
and organize the workflows around them. Issues are often inter-connected via
different links such as the default JIRA link types Duplicate, Relate, Block,
or Subtask. While previous research has mostly focused on analyzing and
predicting duplication links, this work aims at understanding the various other
link types, their prevalence, and characteristics towards a more reliable link
type prediction. For this, we studied 607,208 links connecting 698,790 issues
in 15 public JIRA repositories. Besides the default types, the custom types
Depend, Incorporate, Split, and Cause were also common. We manually grouped all
75 link types used in the repositories into five general categories: General
Relation, Duplication, Composition, Temporal / Causal, and Workflow. Comparing
the structures of the corresponding graphs, we observed several trends. For
instance, Duplication links tend to represent simpler issue graphs often with
two components and Composition links present the highest amount of hierarchical
tree structures (97.7%). Surprisingly, General Relation links have a
significantly higher transitivity score than Duplication and Temporal / Causal
links. Motivated by the differences between the link types and by their
popularity, we evaluated the robustness of two state-of-the-art duplicate
detection approaches from the literature on the JIRA dataset. We found that
current deep-learning approaches confuse between Duplication and other links in
almost all repositories. On average, the classification accuracy dropped by 6%
for one approach and 12% for the other. Extending the training sets with other
link types seems to partly solve this issue. We discuss our findings and their
implications for research and practice.
