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Managing software projects gets more and more complicated with an increasing project and product size. To cope with this complexity, many organizations use issue tracking systems, where tasks, bugs, and requirements are stored as issues. Unfortunately, managing software projects might remain chaotic even when using issue trackers. Particularly for long lasting projects with a large number of issues and links between them, it is often hard to maintain an overview of the dependencies, especially when dozens of new issues get reported every day. We present a Jira plug-in that supports developers, project managers, and product owners in managing and overviewing issues and their dependencies. Our tool visualizes the issue links, helps to find missing or unknown links between issues, and detects inconsistencies.

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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 lifecycle of single issues, although issues have and express dependencies on other issues that constitute issue dependency networks in 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 the Design Science methodology, consisting of eliciting drawbacks and constructing and evaluating 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 detections complement the issue graph by proposing missing dependencies, while consistency checks and diagnoses identify conflicting issue priorities and release assignments. Jira's plugin and service-based system architecture realize the functional and quality concerns of the system implementation. We show how to adopt the intelligent supporting techniques of an issue tracker in a complex use context and a large data-set. The solution considers an integrated and holistic system view, practical applicability and utility, and the practical characteristics of issue data, such as inherent incompleteness.

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