If you have enough data, cool things can be done with estimations - allowing for much more granularity than splitting issues by type and assigning individual placeholder, like we've seen in the previous example. You could create a much more contextual system, that would use historical data from your issue tracker (Jira, Trello, etc.) to predict how long a certain fix would take using Machine Learning, Natural Language Processing and other approaches.
In fact, a group of researches all the way back in 2007 did just that. They came up with a system that automatically predicts the fixing effort. Given a sufficient number of issue reports, their automatic predictions for bugs were off by only one hour, beating human predictions by a factor of four. You can read more about it here