  An issue tracker is a software tool used by organisations to interact with
users and manage various aspects of the software development lifecycle. With
the rise of agile methodologies, issue trackers have become popular in open and
closed-source settings alike. Internal and external stakeholders report,
manage, and discuss "issues", which represent different information such as
requirements and maintenance tasks. Issue trackers can quickly become complex
ecosystems, with dozens of projects, hundreds of users, thousands of issues,
and often millions of issue evolutions. Finding and understanding the relevant
issues for the task at hand and keeping an overview becomes difficult with
time. Moreover, managing issue workflows for diverse projects becomes more
difficult as organisations grow, and more stakeholders get involved. To help
address these difficulties, software and requirements engineering research have
suggested automated techniques based on mining issue tracking data. Given the
vast amount of textual data in issue trackers, many of these techniques
leverage natural language processing. This chapter discusses four major use
cases for algorithmically analysing issue data to assist stakeholders with the
complexity and heterogeneity of information in issue trackers. The chapter is
accompanied by a follow-along demonstration package with JupyterNotebooks.
