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Issue management is a costly part of software development. In large projects, the continuous inflow of issue reports contributes to the information overload in a project, i.e., "a state where individuals do not have time or capacity to process all available information". In issue triaging, an initial step in issue management, a developer must be able to overview existing issue reports and easily navigate the software engineering project landscape. In this presentation, we present support for two work tasks involved in issue management: 1) issue assignment and 2) change impact analysis. We use machine learning to harness the ever-growing number of issue reports, by training recommendation systems on previous issues. Our industrial evaluations on 50,000+ issue reports in two large software development organizations indicate that automated issue assignment performs in line with current manual work. Moreover, we present how traceability from already resolved issue reports to various artifacts can be reused to jump start change impact analyses for newly submitted issues. Finally, we speculate on future ways to tame information overload into helpful software engineering recommendations.
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