Features break after updates when code changes have unexpected side effects and there are no automated tests to catch them, so regressions reach users and fixed bugs keep coming back.
What it means when features break after updates
Every release fixes something and breaks something else. A change to scoring breaks the leaderboard, a new form breaks sign-up, or a bug you fixed last month is back.
This is what happens when a codebase has no automated tests. Every change depends on someone remembering to check everything by hand, and nobody can. Tests that run on every change are the fix.
Symptoms: you're in the right place if
Users report bugs in features that used to work
Bugs you fixed come back
Releases need a long manual checklist
Developers avoid touching certain parts of the code
Numbers differ between screens after changes
The app was built quickly or with AI tools and has no tests
If the same kind of bug keeps returning, the problem isn't the developers. It's the missing safety net.
Business function
Business functions affected: Engineering and Product.
Typical owner: CTO or lead engineer.
Common stage: Fast-moving startups, apps built with AI tools, and codebases inherited from other developers.
Is this the right help for you?
A good fit if:
Releases regularly break existing features
You have few or no automated tests
You release often and want to keep doing so
You're about to refactor or upgrade
Not the right fit if:
The whole codebase needs restructuring: see technical debt slowing team
Nobody understands the code at all: see codebase nobody understands
First steps to stop regressions
List the 5 flows that would hurt most if they broke
Add a test for every bug you fix from now on
Run a quick regression check before each release
Run any existing tests automatically on every pull request
Business impact: what it costs to leave it unfixed
Lost users: people leave apps that keep breaking.
Slower releases: manual testing grows with every feature.
Hotfix cycles: time goes to firefighting instead of building.
Fear of change: important improvements get postponed.
Root causes: why features keep breaking
No automated tests
Nothing checks old features when code changes.
Hidden dependencies
A change in one place affects another.
Tests not in CI
Tests exist but don't run before merging.
Fixes without tests
Bugs return because nothing guards against them.
Common regression scenarios
Scenario | Usual cause | First fix |
|---|---|---|
Leaderboard wrong after scoring change | Shared logic, no unit tests | Unit tests on scoring logic |
Sign-up breaks after a UI update | No end-to-end tests | Playwright tests on sign-up |
Mobile app breaks after backend change | No API contract tests | API tests in CI |
Fixed bug returns | No regression test | A test for every fix |
Release safety checklist
Every “no” is a likely gap.
Tests
Do your critical flows have automated tests?
Does every bug fix get a test?
Automation
Do tests run on every pull request?
Do failing tests block merges?
Releases
Is there a regression check before release?
Can you roll back quickly?
Potential solution family
Solution type: Stabilize. We add the tests that stop releases from breaking things.
Solution family: Test automation and QA: end-to-end, API, unit and regression tests running in CI.
How we fix it: building a safety net
1. Find the risks
Map critical flows and past bugs.
2. Add end-to-end tests
Cover the flows users depend on.
3. Add API and unit tests
Cover backend logic and calculations.
4. Run tests in CI
Every pull request is checked, and failures block merges.
5. Regression checks
A quick run before each release.
6. Hand over
Your team adds tests with each change.
Typical timeline
Phase | Typical duration |
|---|---|
Functional test pass | 1–3 days |
End-to-end tests for key flows | 1–3 days |
CI pipeline | 1 day |
Broader coverage | 1–3 weeks |
A basic safety net for the most important flows can be in place within a week.
Mistakes to avoid
Asking for more careful developers instead of adding tests
Writing tests that don't run automatically
Testing everything end to end instead of focusing
Ignoring flaky tests
Required skills
End-to-end testing: key user flows.
Regression testing: keeping old features working.
Test automation: unit, API and CI.
QA for AI-built apps: apps built with AI tools.
Relevant technologies
End-to-end: Playwright and Cypress
Unit and API: Jest, Vitest, pytest and Postman
CI: GitHub Actions and GitLab CI
Diagnosis offer: start with fixed-price Playwright tests
Get automated tests on your most important flows, running on every change.
What you get:
Tests for up to 5 key flows
CI integration
Screenshots and traces on failure
A guide for adding tests
Proof
Example engagement: streak and leaderboard bugs that kept returning
An illustrative example based on the pattern we see most often. Client details are kept confidential.
The situation: A learning platform's streak bonus and leaderboard broke after almost every release.
What we found:
No automated tests
Streak logic duplicated in two places
No regression check before releases
What we changed:
Added unit tests for streak logic
Added Playwright tests for submission and leaderboard flows
Ran tests in GitHub Actions as required checks
The result: The next regression was caught in a pull request, and releases stopped breaking streaks.
Why buyers trust Codersarts
Delivering software, cloud and security engineering for clients worldwide since 2018
A managed in-house engineering team, not a freelancer marketplace
Security engineers who are also developers, so we fix what we find
Testing only with written authorization and agreed scope
Confidential by default: we sign NDAs before accessing your code or accounts
Frequently asked questions
Why do features break after updates?
Code changes have side effects, and without automated tests nothing catches them before release.
Which tests should we add first?
End-to-end tests for your most important flows, plus a test for every bug you fix.
Do we need a QA team?
Not necessarily. Automated tests and a short regression check cover most needs for small teams.
Can you test an app built with AI tools?
Yes. AI-built apps benefit most from tests, because changes are generated quickly.
Related problems
Codebase nobody understands: when changes feel risky.
Technical debt slowing team: deeper code problems.
MVP not production ready: apps launched before testing.
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