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We’ll take the example of fictive software company Mob&Apps, which designs mobile apps. Their app development project can have different components, for example user interface (UI) and data. These components divides their work into broad categories, but the project team might require further classification, especially in the form of tagging. This is where labels come in.
Why labels are useful
Labels are searchable. For instance, you can search for issues that have been given a particular label. However, when searching, it’s important to note that labels are case-sensitive (for all Jira versions before 7.10), and therefore, the text has to be an exact match in order for the search (and filters) to work as expected.
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If Mob&Apps developers want to associate issues according to their statuses, even if the issues don’t belong to the same component, they can mark issues as “ready_for_UAT” or “needs_review” to be able to find them easily.
Adding and removing labels in Jira issues
Follow these steps:
1- Open the issue you wish to label.
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Mob&Apps developers can’t create labels such as “ready for UAT”. They have to choose between “ready_for_UAT” and “ready-for-UAT”, or use another, similar approach. This latter label, for instance, can be used to tag all issues representing newly developed features that are ready to be rolled out to end users for acceptance testing.
Removing a Jira label
To remove a label from the list of labels, you must remove the label from all the issues (using the method explained above). Once the label has been thus removed, it will no longer be visible in the list of labels.
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Like any other tool, labels can indeed be misused. Any authorized user can add labels to an issue as long as the label field is available on the issue. Consequently, it’s easy to end up with a very large number of labels. The danger here is that the project can end up with numerous capitalization and spelling variations, e.g., login vs. Login, user vs. User, ready_for_UAT vs. ready_for_uat, etc. This can, in turn, render searching via labels inefficient and prone to error. Below, we’ll share our best practices for avoiding this situation.
Atlassian Expert good practices
To best take advantage of the benefits of labels and minimize potential pitfalls, here are some recommendations you can share with your team:
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