SOLUTION · SOFTWARE QA, TESTING AND MAINTENANCE
Automated API and regression testing before every release
Automated API tests are scripts that send requests to your endpoints and check the responses on their own — status codes, data, errors, permissions and business rules — every time the code changes, to confirm that what worked still works. I did development and QA at ZazPay, a fintech where an API bug affects real payments, and I bring that experience to tests that run on every change.
What’s happening today
In many small teams, testing is done by hand before a release: someone opens the app or Postman and checks the main flows. That works while the system is small and there's time. Under pressure, less gets checked, people assume "we didn't touch that", and bugs show up in production — reported by an annoyed user or, worse, by nobody at all.
The most expensive failures tend to hide in edge cases: invalid data, amounts with decimals or negative values, dates in another time zone, permissions for a user who shouldn't see certain information, duplicate requests from a double click or a network retry. Those are exactly the cases a rushed manual check skips, because testing the happy path is the natural thing to do.
Every change can break something far away. A response format is adjusted for the mobile app and a customer's integration that relied on the old format stops working; a validation is fixed and a flow nobody connected to it breaks. Without regression tests, those effects are discovered late.
Automated tests don't replace human judgment or exploratory testing, but they make sure that what already worked is verified on every change, without relying on someone remembering. And they block a deployment before it reaches production, which is when a bug is cheapest to fix.
How I’d solve it
- 01
Identify critical flows
We start with what hurts most if it fails: payments, sign-up and login, permissions, business calculations and third-party integrations. You don't need to test everything on day one; you need to protect what costs money or trust first.
- 02
Contract, business-rule and error tests
I write tests that check status codes, data structure, validations and business rules, for both success and failure: invalid data, missing permissions, non-existent resources and repeated requests. Every protected endpoint must reject anyone without access.
- 03
Test data and environments
I set up reproducible test data and, if needed, an isolated Docker environment so tests never touch real data or depend on the state of a shared database. External services are mocked when they don't offer their own sandbox.
- 04
Continuous integration
Tests run automatically on every change with GitHub Actions or whatever CI you already use, and block the deployment if something fails. The team finds out in minutes, not when a customer complaint arrives.
- 05
Clear reports and room to grow
When a test fails, the report says what was expected, what happened and how to reproduce it. I leave the structure and documentation so your team can add a new test every time they fix a bug, so the same bug never comes back.
Example: a payment registration endpoint
- Valid payment test: returns success and the record is stored with the exact amount.
- Negative, zero or malformed amount: returns a validation error and stores nothing.
- Duplicate request with the same idempotency key: it isn't recorded twice.
- No token, or a user from another merchant: access is denied.
- Simulated bank service failure: the payment stays pending, not confirmed.
- All of them run on every change and block the deployment if any fails.
Related work
Go deeper
Frequently asked questions
Does my project need existing tests to start?
No. I can start from scratch with the most critical flows and leave the structure ready for your team to add more. In fact, that's the most common situation: many production systems have no automated tests at all, and you start by protecting what matters most.
What's the difference between Postman tests and code-based tests?
Postman with Newman turns request collections into tests that run in CI, and it's a good start if your team already uses Postman. Code-based tests (Pytest, Jest) give more control to prepare data, mock services and test complex logic. I choose based on your team and stack; sometimes both coexist.
Which languages and frameworks do you cover?
I work mainly with Node.js/NestJS and Python APIs. HTTP-level API tests work regardless of the backend's language, so I can also test APIs built in PHP, Java or others, though for fixing those systems' code I'll tell you honestly whether I'm the right person.
Do automated tests replace a QA tester?
Not entirely. Automated tests are great at confirming that known behavior still works; a person's exploratory testing finds what nobody thought of. The ideal is to combine both: automate the repetitive work so human time goes into exploring.
What determines scope and cost?
The number of critical endpoints, the complexity of the business rules, whether test data or environments need to be prepared, external services to mock and integration with your existing CI. Protecting the five or ten most important flows is a well-bounded project that already changes your risk a lot.
This solution is part of my software qa, testing and maintenance service.
Want to talk it through?
If your situation sounds similar, tell me how you work today. I’ll tell you which part is worth automating or building first.