How to export linked issue with a certain issue type?

[Binesh John]

How to export linked issue with a certain issue type filter applied?

For Ex: I have 20 stories in a sprint and each of the stories have multiple linked issues like bugs, tests, tasks. I need to filter out only the linked bugs for each of the story

Issue key "ABC-100" is a story that is linked to a bug ABC-101 and task ABC-102

Issue key "ABC-103" is a story that is linked to only a bug ABC-104

Issue key "ABC-105" is a story that is linked to only a task ABC-106

Now I need to export a CSV with all stories that has a linked bug with the relevant bug IDs

Expected filter result for above example should be like below,

ABC-100 linked issue ABC-101 (ABC-102 should not be displayed as it not a bug)

ABC-103 linked issue ABC-104

ABC-105 should not be exported as there are no linked bugs

[Ivan Lima · Accepted solution]

Hi @Binesh John. To my knowledge, you cannot achieve that out of the box. You can find issues linked to others by a particular link type—for example, issue in linkedIssues(ABC-123, "is duplicated by"). However, you will get all linked issues (Outward issue link) when you export them. For example, if you're exporting to a CSV file, you will see one column per link type but not the issue type. Perhaps, other community members can chime in with their thoughts on apps that could help you achieve that.

[Binesh John]

Thank you @Ivan Lima 

Yes, I can extract all linked issue in to the CSV. But it would not be useful for my use-case where I wanted to find only the linked bugs for each story ignoring the linked tasks or tests.

[Ivan Lima]

I understand you want to extract only bugs linked to issues. Unfortunately, Jira hasn't yet a feature that allows you to iterate over the results to pull only linked issues of a particular issue type. In this case, you would need a 3rd party app such as Xporter - Export issues from Jira. Please, look at their documentation; you might be looking for iterations and how to apply filters to iterations.

[Binesh John]

Thanks @Ivan Lima