How to Use Devin to Clear a Backlog Ticket End to End

May 12, 2026 · Editorial Team · 5 min read · devin ai-coding software-development

Most engineering backlogs have a category of tickets that nobody touches: the small, well-defined, slightly tedious ones. Add a field to this API response. Update these 30 unit tests to match the new interface. Write a migration script for this schema change. Every engineer knows how to do these, nobody wants to spend a sprint on them, and they pile up. Devin's job is to work through that pile.

Devin is a software engineering agent that can read a ticket, write code in your actual repo, run tests, fix failures, and open a pull request. The human does the review. Here's how to use it effectively and what to expect when it doesn't go perfectly.

Getting Access and Connecting Your Repo

Devin is available through the Cognition web app at app.devin.ai, and also via a Slack integration that lets you assign tickets directly from Slack threads. If you're using Jira or Linear, the integration connects Devin to your issue tracker so it can read ticket details and context.

To connect your repository:

In the Devin dashboard, go to Integrations and connect your GitHub (or GitLab) account.
Grant Devin access to the specific repo you want it to work in. You can scope this to individual repos rather than your entire organization.
Devin will clone the repo, read the codebase structure, and index it so it can navigate the code intelligently.

The first session in a new repo takes longer because Devin is building its understanding of the codebase. It will read README files, look at the directory structure, and run any setup commands it finds in the documentation.

Assigning a Ticket: What Works

You can assign a ticket in three ways:

Via the Devin web app: Paste or describe the task directly in the session interface. You can copy-paste the entire ticket body from Jira or Linear.

Via Slack: If the integration is set up, mention @Devin in a thread and describe the task. Devin picks up the request and opens a session.

Via issue tracker integration: In Jira or Linear, you can assign tickets directly to Devin as though it were a team member. It polls for new assignments and starts working automatically.

For most teams, the Slack method is the easiest starting point. You can include the ticket text, link to relevant files, and add context all in one message.

Scoping Tickets for Best Results

This is the most important factor in whether Devin succeeds or wastes your time. I've watched Devin handle dozens of tickets and the pattern is clear: small, well-defined tasks with unambiguous success criteria finish cleanly. Broad, open-ended tasks produce partial work that requires significant human follow-up.

Good ticket to assign to Devin:

Add a `last_login_at` timestamp field to the User model. 
Update it when a user successfully authenticates. 
Include a migration. Update the user serializer to include this field 
in the API response. Add unit tests covering the migration and the 
serializer update. The existing test suite uses pytest.

Ticket that will cause problems:

Improve the performance of the user dashboard

The second ticket requires architectural judgment, profiling, and decisions about trade-offs that Devin can't make without more context. It will do something, but probably not what you intended.

Here's a quick framework for deciding what to assign:

Ticket characteristic	Good for Devin
Clear inputs and outputs	Yes
Touches 1-3 files	Yes
Has a passing test suite to validate against	Yes
Requires understanding business context	No
Involves UI/UX decisions	No
Needs coordination with other in-progress work	No
Unclear or contradictory requirements	No

Watching Devin Work

Once you assign a ticket, Devin opens a session you can observe in real time. You'll see:

A terminal showing commands it's running
A code editor view showing files it's writing or modifying
A browser if it needs to look something up
A running log of its reasoning and decisions

You don't need to watch this. It's there for transparency, and it's genuinely useful when something goes wrong and you need to understand where it got confused. For routine tickets, check back in 10-30 minutes.

Devin will ping you (in Slack or the web app) if it hits a blocker it can't resolve. Common blockers: it can't find a test database, environment variables are missing, or it encounters a design decision where it genuinely can't tell what the right call is. When it asks, answer quickly. A blocked session that waits too long sometimes loses context and needs to restart from scratch.

Reviewing the Pull Request

When Devin finishes, it opens a PR on your main branch. The PR description usually includes:

A summary of what it did
A list of files changed
Notes on any decisions it made that weren't specified in the ticket

Review this PR the same way you'd review a junior engineer's work. Don't rubber-stamp it just because a machine wrote it. Specifically check:

The tests actually test what they're supposed to test. Devin sometimes writes tests that pass trivially without actually validating the behavior. Read the assertions.
Edge cases are handled. If the ticket said "update the user serializer", check what happens when the new field is null or missing.
No unintended changes. Occasionally Devin modifies files outside the scope of the ticket while "cleaning up" related code. Check the diff carefully.
The migration is reversible. Any database migration should have a down migration. Verify it's there and correct.

If something's wrong, leave a comment on the PR. Devin reads PR comments and will push fixes. You can have a normal back-and-forth code review with it through the PR comments interface.

Where Devin Genuinely Shines

After working through a backlog with Devin, the task types that work best are:

Schema changes and migrations. Add a column, update the model, update the serializer, write the migration, update tests. Devin handles this sequence cleanly and reliably.

Test coverage gaps. "Write unit tests for this module that currently has no tests." Devin reads the code, writes tests, and achieves reasonable coverage. You may need to adjust edge cases, but the bulk of the work is done.

Documentation generation. Generating docstrings or API documentation from existing code. Not glamorous, but it genuinely saves hours.

Dependency updates. Updating a library to a new major version and fixing the resulting breakages. Tedious, repetitive, perfect for Devin.

Boilerplate tasks. Adding logging to a set of functions, converting a set of files to a new format, renaming a field across multiple layers of an application.

The common thread is that these tasks are unambiguous about what success looks like and boring enough that engineers keep deprioritizing them. That's exactly the backlog Devin was built for.