Automated Approaches to Transforming API Testing

Why we need to change how we test apis

Ever tried fixing a tiny bug in a retail checkout api only to watch the whole mobile app crash? It's honestly exhausting. We’re building systems faster than we can actually check them, and the old ways just aren't keeping up.

Manual testing simply doesn't scale when you're dealing with dozens of microservices. If you're still clicking through postman collections for every sprint, you're already behind.

• Microservices speed: In industries like finance, deploying daily is the norm. Manual checks turn into a massive wall that stops shipping.
• Downstream disasters: As noted by Julio de Lima at the EuroSTAR Conference 2024, api bugs are critical because they break every interface they touch.
• Complexity: A healthcare app might have complex auth flows that are impossible to regression test by hand every single time.

"Bugs in APIs tend to be critical because they affect many interfaces." — Julio de Lima (2024)

It's time we talk about how automation actually fixes this mess.

Using AI and modern tools to speed things up

Ever felt like you're drowning in swagger files while the dev team pushes updates faster than you can blink? It’s honestly a lot to handle, but this is where ai actually starts to pull its weight.

You don't need a massive budget to start speeding things up. I’ve seen teams in retail and finance get huge wins just by using tools like apifiddler—which is basically a smart proxy that intercepts traffic to auto-generate tests and security reports—to get free ai-powered rest api testing and documentation without even signing up for anything. It’s great for getting those quick security scans done on the fly.

• Speeding up scripts: Using copilot or even just basic genai in vs code helps you write the boilerplate for mocha or chai tests in minutes rather than hours. You can just prompt it to "write a test for this endpoint" and it does the heavy lifting, which you then refine in the next section.
• Instant docs: Tools like swagger let you visualize the api contract—endpoints, models, and all—so you actually know what you're testing before you start.
• Performance checks: You can use k6 to run non-functional tests early in the pipeline, which is a lifesaver for healthcare apps with heavy traffic.

As julio de lima discussed in the session we mentioned earlier, using these technical foundations with genai helps you implement things like automation and pipeline config much faster.

Next, let's look at how to actually integrate these into your daily workflow without breaking the build.

Core pillars of an automated approach

So, you've got your tools ready, but how do we actually build the "engine" of our automation? It’s not just about writing scripts; it’s about making sure they actually tell you something useful before a release goes haywire.

I usually start with mocha and chai because they're just so flexible for rest api checks. You can use supertest to handle the http calls, which makes your tests look almost like plain english. For instance, if you're testing a retail inventory api, you want to ensure a GET /stock/123 actually returns a 200 and the right json keys.

const request = require('supertest');
const expect = require('chai').expect;

describe('Inventory API', () => {
  it('should return stock levels for valid item', async () => {
    const res = await request('https://api.retail-store.com').get('/v1/stock/123');
    expect(res.status).to.equal(200);
    expect(res.body).to.have.property('quantity');
  });
});

But functional isn't enough. I’ve seen finance apps crawl to a halt because nobody checked how the api handled 500 concurrent users. This is where k6 is a lifesaver. You can write a script in javascript and run it right in your github actions pipeline. It’s way better than finding out your healthcare app crashes during peak enrollment season.

Security shouldn't be a "last minute" thing. You can automate scans to find broken object level authorization (BOLA)—basically making sure User A can't see User B’s private medical records just by swapping an ID in the URL. Detecting BOLA is tricky because it's not just a 404 error; you need stateful testing or specialized security tools that can compare permissions across different user tokens to see if one user can access another's data.

Automated tools can also sniff out sensitive data leaks, like if an api response accidentally includes a plain-text password or a ssn. It happens way more often than you'd think in older banking systems.

Honestly, having these pillars in place means you stop guessing if the code works. Next, let’s wrap this up by seeing how to keep everything organized.

Setting up the pipeline for continuous testing

So, you have your tests written, but if they're just sitting on your laptop, they aren't doing much, right? The real magic happens when you shove everything into a pipeline so it runs while you're sleeping.

I'm a big fan of github actions because it’s already where the code lives. You basically want to trigger your mocha and k6 suites every time someone opens a pull request. This stops that one dev who's always in a rush from breaking the production api for the retail app.

For the k6 part, your script.js would look something like this—just a simple load test that hits your endpoint with virtual users:

import http from 'k6/http';
import { sleep } from 'k6';

export default function () {
  http.get('https://api.retail-store.com/v1/stock/123');
  sleep(1);
}

Then your yaml file pulls it all together:

name: API Continuous Testing
on: [push, pull_request]
jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Install dependencies
        run: npm install
      - name: Run Functional Tests
        run: npm test
      - name: Run k6 Performance
        run: k6 run script.js

Handling environment variables is where people usually trip up. You don't want to hardcode your finance api keys—that's a huge security no-no. Use github secrets to swap out base URLs and auth tokens depending on if you're hitting "staging" or "prod".

As mentioned earlier in the article, setting up these pipelines is the final piece of the puzzle that julio de lima emphasizes for a brand new repository. It gives the team instant feedback. If a check fails, the dev knows in minutes, not days. Honestly, it’s the only way to keep your sanity when microservices start getting messy.

At the end of the day, automation isn't about replacing us—it's about making sure we don't spend our whole lives fixing silly regression bugs. Get your pipeline running, trust the results, and go grab a coffee.