Definition of done for bug fixes: a practical checklist

What “done” means for a bug fix (in plain words)

A bug isn’t done when the code compiles, or when it works on one laptop. “Fixed on my machine” usually means the test was too narrow, the setup was different, or an edge case was missed. Real users don’t run your exact browser, data, network, or permissions.

Closing a bug too early is how small issues turn into bigger ones: support tickets spike, teammates stop trusting releases, and quick patches pile up on top of quick patches. The real cost often isn’t the original bug. It’s the time spent re-learning the same problem and cleaning up the mess.

For a bug fix, “done” should mean four things: verified, safe, observable, and explainable.

• Verified: you can reliably reproduce the bug, and you can reliably show it’s gone.
• Safe: you considered data loss, security, and unintended side effects.
• Observable: you know what to watch after release, and what would justify a rollback.
• Explainable: someone else can read the ticket and understand what happened, what changed, and why.

A simple example: a checkout total is wrong only for returning users with old coupons. If you only test with a new account, you “fix” nothing for the people actually affected.

Before you touch code: confirm the bug and its impact

A lot of “quick fixes” fail because the team fixes the wrong thing. Separate what the user sees (the symptom) from what you think is broken (the cause). The symptom is a fact. The cause is a guess until you prove it.

Write down the impact before you open your editor. If this bug happens once a month to one user, you handle it differently than a bug that blocks signups today. Severity isn’t “how bad it sounds.” It’s how many people are affected, how often, and what they can’t do.

Capture the exact context. Many bugs are real only in a specific environment: a certain browser version, a mobile device, a staging database, or a new release. If you can’t name where it happens, you can’t confidently say it’s fixed.

A quick pre-code checklist you can paste into a ticket:

• Symptom: what the user sees, in their words
• Scope: who is affected and how often
• Environment: app version, device, browser/OS, and account type
• Expected behavior: what should happen instead
• One-sentence problem statement: “When X, Y happens, but Z should happen.”

Example: a founder says “login is broken.” You reproduce it and learn it only fails on Safari after password reset. That changes the work. You’re not “fixing login,” you’re fixing a specific session or cookie flow.

Repro steps that actually reproduce (a simple template)

A bug report is only useful if someone else can make the bug happen. If your repro steps work only for the person who found it, it’s hard to fix and even harder to confirm it’s truly gone.

Write steps as if a new teammate will follow them without asking questions. The key is the starting state: who the user is, what data exists, and what environment you used.

A simple repro template

Use this template and fill it in with real details (not placeholders):

Title: <short, specific>
Environment: <prod/staging/local>, <browser/app version>, <device>
Starting state: <logged in/out>, <account role>, <sample record id>, <feature flags>
Steps to reproduce:
1) ...
2) ...
3) ...
Expected result: ...
Actual result: ...
Evidence: <error message>, <log snippet>, <screenshot description>
Frequency: <always / 3 out of 10>, Triggers: <only after refresh, only on slow network>

When you can, include the exact error text. “Login fails” is vague, but “400: invalid_grant after password reset” gives the fixer a real lead.

If the issue is flaky, say so and describe what seems to set it off. Mention timing, device type, browser extensions, or any recent change (new user, imported data, upgraded plan).

Sanity check your own steps by handing them to someone else. If they can’t reproduce within 5 minutes, the report needs one more pass.

Acceptance criteria: how you will know it is fixed

Acceptance criteria is the shared answer to: “What should happen after we ship this?” For bug fixes, it turns “I think it’s fixed” into “we can prove it’s fixed.”

Describe what “good” looks like in plain, testable terms. Tie it to the user outcome, not the implementation. Example: “User can log in with a correct password and is taken to the dashboard within 2 seconds.”

Then call out the edge cases that often get missed: empty states, timeouts, bad input, retries, and partial failures.

A simple checklist you can copy:

• Primary path works end-to-end
• Known edge cases are handled (empty input, invalid values, slow network, retry)
• No new errors in logs and no new warnings in the UI for the same flow
• UI behavior is clear (messages, button states, loading indicators, redirects)
• Data stays correct (no duplicates, no partial writes, no surprise deletions)

Add non-goals to stop scope creep. Example: “We are not changing the password policy in this fix,” or “We are not redesigning the login page, only correcting error handling.”

If the fix changes text or behavior, write it down. “Wrong password” vs “Email not found” isn’t just wording. It can change support load and security risk.

If possible, get quick agreement from the requester. A 2-minute confirmation saves a day of rework.

A step-by-step workflow for closing a bug safely

A good close is less about the code change and more about proving the bug is gone without creating a new one.

1. Reproduce and capture evidence. Screenshot, exact error text, and the environment (app version, browser/device, user role, feature flag state). If you can’t reproduce, tighten the report before you guess.

2. Isolate the smallest likely cause. Find the one condition that flips the behavior (one input, one account state, one API response). This prevents fixes that “work” by accident.

3. Make the smallest safe change. Avoid refactors during a bug fix unless the mess is the cause. Small changes are easier to review, test, and roll back.

4. Verify in two places. Test locally, then test in a clean environment that matches production settings as closely as possible.

A quick closure checklist for the ticket:

• Repro confirmed and evidence saved
• Root cause stated in one sentence
• Minimal change made (and why it’s safe)
• Verified locally and in a clean environment
• Notes added: what changed, what didn’t, and risk areas

Finally, add rollout and monitoring notes: how to deploy safely, what metrics or logs should improve, and what would mean you should roll back (for example, a spike in 500 errors or login failures within the first hour).

Tests: what to add, what to rerun, what to document

A bug isn’t really done until the tests tell the same story as your repro steps. Aim to leave behind at least one automated check that would have failed before the fix and now passes.

Add or update a test that matches the real failure. Keep it small and focused so it stays readable and doesn’t become flaky. Cover the happy path, then add one edge case that represents how the bug slipped through (missing field, timing issue, stale cache, rare user setting).

After your targeted test passes, do a short regression pass around nearby flows you might have touched:

• The main user journey that includes the changed code
• One adjacent flow (for example, signup if you fixed login)
• A basic permissions check (authorized vs unauthorized)
• For UI fixes: verify on two browsers/devices you actually support
• For backend fixes: try one bad input that should fail safely

Document what you ran and what you didn’t. If you’re accepting a test gap, name it and explain why (time, tooling, missing fixture, unclear requirements), plus what would catch it later (monitoring alert, follow-up task, manual checklist).

Example: if a login fix involved token parsing, add a test for a valid token and one for a malformed token that must return a clean error without logging secrets.

Safety checks: security and data risks to rule out

A bug can be “fixed” and still create a bigger problem: a data leak, a permission bypass, or logs full of private info. This is the part people skip when they’re rushing.

Scan what changed around inputs, auth, and anything that touches the database. Many incidents come from small edits like “just adding a parameter” or “logging more details to debug.”

Before you mark the ticket done:

• Secrets: confirm no API keys, tokens, or credentials were added to code, config, error messages, or client-side bundles.
• Input handling: validate and sanitize inputs, especially anything used in queries, filters, or sorting. Use safe query patterns to avoid SQL injection.
• Auth and permissions: re-check that the fixed path still enforces login and role checks (and that error handling doesn’t accidentally skip them).
• Logging: make sure logs don’t include passwords, session tokens, reset links, or personal data. If you need context, log an ID, not the raw value.
• Abuse cases: if an endpoint or flow changed, consider rate limits and brute force attempts (login, password reset, promo codes, OTPs).

A concrete example: you fix a login bug by changing the error response to include “helpful details” (like whether an email exists). That can enable account enumeration. Prefer a generic message, and keep details internal.

Ask a few final questions before closing:

• Could this change expose data to the wrong user?
• Could it make it easier to guess accounts, tokens, or passwords?
• Could the new logs leak anything sensitive?
• Could a bot hit this faster than a human?

Rollout notes: what to write so releases don’t surprise anyone

Rollout notes are the “heads up” that turns a code change into a safe release. They let someone who didn’t work on the bug understand what changed, how it ships, and what to do if anything looks wrong.

Start with a short summary for non-technical readers: what was broken, what users will notice now, and who is affected. If the bug touched billing, login, or permissions, say that plainly.

Then capture the deploy-time risk points:

• Config changes (new env var, changed value, secret rotation)
• Database migrations (what runs, how long it might take)
• Feature flags (name, default state, who can toggle)
• Rollout plan (all at once, canary, phased by % or region)
• Rollback plan (what you will revert and how you will verify)

Be specific about rollback triggers. “Rollback now” should mean something observable, like a spike in login failures, a jump in support tickets, or a clear new error in logs. Note whether rollback is safe with any migration (some schema changes aren’t easily reversible).

Also list customer-facing changes, even small ones: UI text, emails, notifications, or error messages. Support teams rely on this.

Example: “Login fix improves session handling for legacy users on Safari. No DB changes. Added a new env var for the email provider timeout. Rollout: 10% for 30 minutes, then 100% if sign-in success stays normal. Rollback if sign-in errors rise above baseline.”

After shipping: what to monitor and for how long

A bug isn’t done when the code merges. It’s done when the change behaves in the real world, under real traffic.

Set a watch window for every release. For low-risk fixes, 2 to 4 hours may be enough. For anything touching auth, payments, or data, plan 24 to 72 hours. Name one owner for the window (not “the team”), and make sure they can roll back or hotfix if needed.

Focus on signals that show customer pain fast:

• Error rate and top new errors (by endpoint or screen)
• Latency and timeouts (p95 is often more telling than averages)
• Sign-in failures and password reset failures
• Payment failures and checkout drop-offs
• Support tickets and user complaints (they count as monitoring)

Define thresholds that trigger action before you ship, so there’s no debate later. Examples: “login failures up 2x over baseline for 10 minutes” or “a new error appears in the top 5.” Pair alerts with a simple response: investigate, roll back, or pause the rollout.

Also watch for quiet but dangerous changes: fewer completed sign-ins, fewer created projects, more retries. That can mean the bug moved rather than disappeared.

Document what you checked and the outcome: time window, dashboards/logs reviewed, any spikes seen, and the final call.

Common traps that make “fixed” bugs come back

Repeat bugs usually aren’t bad luck. They happen when a fix ships without the proof and communication that turns a change into a reliable outcome.

A common failure is closing the ticket without a clean repro and clear acceptance criteria. If you can’t show the bug on demand, you can’t be sure it’s gone, and you can’t be sure you fixed the right thing.

Another is fixing the symptom and skipping the root cause. Example: adding a retry button for a failing request when the real issue is an expired token that never refreshes. The retry hides the bug and trains users to keep clicking.

The traps that most often undo a bug fix:

• No reproducible steps or vague “works for me” checks
• No test added (or the wrong test), so a later change reintroduces the bug silently
• A “small fix” that sneaks in a big refactor, increasing risk and making review harder
• Skipping rollout notes, so support and teammates don’t know what changed
• Only verifying the happy path, not a realistic environment

That last point deserves extra attention. A fix can pass locally but fail in staging because of real data, missing env vars, caching, or different auth settings. Always try a fresh account, a clean browser session, and the slowest or oldest device you can reasonably test.

Example: closing a login bug without breaking production

Scenario: after an AI-built update, some users report that sign-in fails. It works for the developer, but not for real customers.

Bad repro steps look like this: “Login is broken sometimes.” That wastes hours because no one can reliably see the failure.

Good repro steps are specific and repeatable:

1. Use an existing user created before the last release.
2. Log out, then try to sign in on mobile Safari.
3. Enter correct email and password.
4. Observe: after submitting, the page reloads and you are still logged out (no error shown).

Acceptance criteria should be just as clear. Bad: “Users can log in again.” Better: “The user reaches the dashboard, receives a session cookie, and stays logged in after refresh. Incorrect passwords still show the same error message as before.”

For tests, add one focused check and rerun one regression:

• Add an integration test for the sign-in flow (correct credentials create a session and return success).
• Regression check: password reset, logout, and sign-in on the same browser that failed.

Rollout notes can be short but concrete: “Fix session handling for legacy users on Safari. No DB changes. If issues appear, roll back to prior build and invalidate sessions.” After shipping, watch login error rate, new session creation, and support tickets for 24 to 48 hours.

The ticket is truly done when the bug reproduces before the fix, can’t reproduce after the fix, tests cover the failure, and rollout notes say what changed and what to watch.

Next steps: make this your default, and get help when needed

Make this stick by turning it into a default habit. Pick one place where every bug lives (your ticket template, a shared doc, or a form) and paste a one-page “definition of done” there so no one has to remember it.

Do one short team check-in to align on what “done” means. Quiet differences matter: one person thinks “repro + fix” is enough, another expects tests, rollout notes, and a post-release watch plan. Getting agreement once prevents reopenings later.

Try the checklist on the next three bug fixes and adjust based on what felt heavy or unclear. Keep it short, but non-negotiable.

A simple starter you can copy into your bug template:

• Repro steps confirmed (and saved), plus expected vs actual
• Acceptance criteria written in plain language
• Tests added or updated, and key existing tests rerun
• Rollout notes prepared (risk, who to notify, rollback plan)
• Post-release watch plan (what to monitor and for how long)

If bugs keep bouncing back even when you follow this, the root issue is often architectural or security-related, not a single line of code. If you inherited an AI-generated codebase from tools like Lovable, Bolt, v0, Cursor, or Replit, FixMyMess (fixmymess.ai) can run a free code audit to pinpoint issues like broken authentication, exposed secrets, or tangled logic before your next release.