  Issue trackers, such as Jira, have become the prevalent collaborative tools
in software engineering for managing issues, such as requirements, development
tasks, and software bugs. However, issue trackers inherently focus on the
life-cycle of single issues although issues have and express dependencies on
other issues that constitute an issue dependency network in a large complex
collaborative projects. The objective of this study is to develop supportive
solutions for the improved management of dependent issues in an issue tracker.
This study follows Design Science methodology, consisting of elicitation of
drawbacks, and construction and evaluation of a solution and system. The study
was carried out in the context of The Qt Company's Jira, which exemplifies an
actively used, almost two decade old issue tracker with over 100,000 issues.
The drawbacks capture how users operate with issue trackers to handle issue
information in large, collaborative and long-lived projects. The basis of the
solution is to keep issues and dependencies as separate objects and
automatically construct an issue graph. Dependency detection complements the
issue graph by proposing missing dependencies, and consistency check and
diagnosis identify incompatible issue priorities and release assignments.
Jira's plugin and service-based system architecture realizes the functional and
quality concerns of the system implementation. We show how to adopt the
supporting intelligent techniques of an issue tracker in a complex use context
and a large data-set. The solution takes into account integrated and holistic
system-view, practical applicability and utility, and the practical
characteristics, such as inherent incompleteness, of issue data.
