{"id":6033,"date":"2026-03-19T09:33:17","date_gmt":"2026-03-19T09:33:17","guid":{"rendered":"https:\/\/kindgeek.com\/blog\/?p=6033"},"modified":"2026-03-19T09:33:19","modified_gmt":"2026-03-19T09:33:19","slug":"test-automation-strategy","status":"publish","type":"post","link":"https:\/\/kindgeek.com\/blog\/post\/test-automation-strategy","title":{"rendered":"Automation-First: Why We Write Tests Before Code Ships (and Why It Saves Weeks)"},"content":{"rendered":"\n

Subject matter expert:<\/p>\n\n\n\n

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\"Victor<\/a><\/figure>
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Victor Olkhovskyi<\/strong>
Manual\/Automation QA Engineer at Kindgeek<\/p>\n<\/div><\/div>\n<\/div><\/div>\n<\/section>\n\n\n\n

The traditional sequence \u2014 develop, then test, then automate \u2014 is one of the most expensive workflows in software engineering. Here’s what happens when you flip it.<\/p>\n\n\n\n

A complex feature, 15 deploy-and-fix cycles, zero manual retesting \u2014 this is what a well-designed test automation strategy delivers in practice. <\/p>\n\n\n\n

On a white-label banking platform we’ve been building and scaling for years \u2014 now running 60+ microservices and 900+ API endpoints \u2014 we replaced that sequence with something fundamentally different. We call it automation-first: the QA engineer writes automated tests in parallel with the developer writing the feature, not after it ships.<\/p>\n\n\n\n

The results were dramatic enough that we think this approach deserves a much wider audience. Not as theory, but as something we’ve practiced across hundreds of features, production incidents, and release cycles in a regulated fintech environment.<\/p>\n\n\n\n

The Role of Test Automation in Modern Software Development<\/h2>\n\n\n\n

Modern software platforms \u2014 especially in fintech \u2014 release code continuously, run dozens of interdependent services, and operate under strict regulatory constraints. Manual QA cannot scale to match this pace. A structured test automation strategy is the operational backbone that lets engineering teams ship faster without trading quality for speed.<\/p>\n\n\n\n

Manual Testing vs Automated Testing<\/h3>\n\n\n\n

Manual testing and automated testing strategy serve different roles in a healthy QA process. Manual testing excels at exploratory work \u2014 finding edge cases a script would miss, evaluating UX flows, or validating novel business logic. Automated testing excels at repeatability: executing the same 900 regression scenarios after every deploy in under 15 minutes, with zero human effort per run.<\/p>\n\n\n\n

Dimension<\/strong><\/td>Manual Testing<\/strong><\/td>Automated Testing<\/strong><\/td><\/tr>
Execution speed<\/td>Hours per test cycle<\/td>Minutes for full regression<\/td><\/tr>
Repeatability<\/td>Varies by tester<\/td>Identical every run<\/td><\/tr>
Best use case<\/td>Exploratory, UX, new features<\/td>Regression, API, CI\/CD gates<\/td><\/tr>
Cost over time<\/td>Grows linearly with coverage<\/td>Shrinks per-test as suite scales<\/td><\/tr>
Setup effort<\/td>Low initially<\/td>Higher upfront, lower ongoing<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Why Every Engineering Team Needs a Test Automation Strategy<\/h3>\n\n\n\n

The traditional testing workflow in most engineering teams follows a predictable rhythm: develop, test manually, automate if there’s a budget. Here’s what that rhythm actually costs. A developer picks up a ticket. They write the code, open a pull request, merge it. New code deploys to a test environment. A QA engineer reads the requirements, sets up test data, which in fintech means creating users, configuring accounts, seeding balances, sometimes triggering a KYC flow. They execute test scenarios manually. If something fails, they document it, file a bug, move the ticket back to the developer.<\/p>\n\n\n\n

The developer doesn’t pick up the bug immediately. Hours pass. They read the bug report, ping QA on Slack. The QA engineer is busy with another ticket. They eventually connect, walk through the issue, spend time in logs. This cycle \u2014 what engineers call QA-developer ping-pong \u2014 can repeat five, ten, fifteen times on a complex feature. Each round costs 30 minutes to an hour of QA time for setup and verification alone, plus communication overhead, plus context-switching<\/a> for both people. <\/p>\n\n\n\n

Without a clear software test automation strategy, teams feel perpetually behind \u2014 because they are.<\/p>\n\n\n\n

What Automation-First Actually Means<\/h2>\n\n\n\n

Automation-first doesn’t mean “automate everything before you write any code.” It means the QA engineer and the developer work in parallel from the moment a feature enters the sprint.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Sprint planning happens.<\/strong> The QA engineer sees which features are coming. They understand the business requirements, the API contracts being designed, the expected behavior.<\/p>\n\n\n\n

Development and automation begin simultaneously. <\/strong>The developer starts building endpoints and business logic. The QA engineer starts writing automated test scenarios for the same feature, sometimes against placeholder endpoint names that get swapped for real ones once the code is deployed.<\/p>\n\n\n\n

Sometimes the developer ships test endpoints first \u2014 just the API contract with minimal logic \u2014 and automation is built against those while full business logic is still in development.<\/p>\n\n\n\n

The developer deploys.<\/strong> The moment new code hits the test environment, automated tests run against it. Results appear in minutes. If they fail, the developer sees exactly what failed \u2014 which test, which step, what the expected result was, what actually happened, and a direct link to the relevant logs.<\/p>\n\n\n\n

There is no handoff.<\/strong> The ticket doesn’t move to “Ready for QA.” The developer doesn’t wait for someone to pick it up. The feedback loop is measured in minutes, not hours or days.<\/p>\n\n\n\n

When the developer finishes the feature, I finish the automation. He deploys, the tests run automatically. If they fail, he sees why immediately. The ticket doesn’t go to “Ready for QA.” There’s no ping-pong. The developer gets instant feedback on whether the basic scenarios work.<\/p>\n\n\n\n

The 15-Iteration Story<\/h2>\n\n\n\n

The most compelling evidence for this approach came from a single complex feature \u2014 an end-to-end flow involving multiple microservices, third-party integrations, and intricate business logic.<\/p>\n\n\n\n

The QA engineer wrote the automation in parallel with the developer. When the developer deployed the first version, the tests ran automatically. They failed. The developer checked the test report \u2014 step-by-step reproduction, expected versus actual results, log links attached to each step \u2014 and identified the issue on the backend. No Slack messages. No meetings. No waiting.<\/p>\n\n\n\n

The developer deployed a fix. Tests ran again. Failed again \u2014 a different issue this time. Fixed. Deployed. Failed. Fixed. Deployed.<\/p>\n\n\n\n

This happened more than 10 times. More than 10 deploy-and-fix cycles over the course of about a week.<\/p>\n\n\n\n

Here’s the critical part: the QA engineer did essentially nothing during that week. No manual retesting. No setting up test data 15 times. No reviewing logs 15 times. No back-and-forth on Slack explaining how to reproduce the issue. The automated tests provided all the feedback the developer needed, completely self-service.<\/p>\n\n\n\n

During that same week, the QA engineer was already writing automation for the next developer’s feature.<\/p>\n\n\n\n

\u274c Manual Workflow \u00d7 15 iterations<\/strong><\/th>\u2705Automation-First \u00d7 15 iterations<\/strong><\/th><\/tr><\/thead>
1. Dev moves ticket \u2192 “Ready for QA”<\/td>1. Dev deploys new version<\/td><\/tr>
2. QA notices ticket (delay: hours)<\/td>2. Tests run automatically (minutes)<\/td><\/tr>
3. QA sets up test data (30\u201360 min)<\/td>3. Dev opens report: steps, data, logs<\/td><\/tr>
4. QA tests, reviews DB, S3, logs<\/td>4. Dev identifies issue, fixes it<\/td><\/tr>
5. QA moves ticket \u2192 “Test Fail”<\/td>5. Dev deploys \u2192 back to step 1.<\/td><\/tr>
6. Dev picks up (delay: hours)<\/td><\/td><\/tr>
7. Dev asks: “How did you reproduce?”<\/td><\/td><\/tr>
8. Joint debugging session on Slack<\/td><\/td><\/tr>
9. Dev fixes \u2192 deploys \u2192 back to step 1<\/td><\/td><\/tr><\/tbody>
\u2248 2\u20133 weeks to deliver<\/strong><\/td>\u2248 1 week to deliver<\/strong><\/td><\/tr><\/tfoot><\/table><\/figure>\n\n\n\n

Each manual test cycle on this particular flow took 30 minutes to an hour \u2014 setting up test data, checking database state, reviewing S3 buckets, inspecting message queues, verifying logs. Fifteen rounds is 7.5 to 15 hours of pure QA execution time, plus communication overhead. Nearly two full working days consumed retesting a single feature.<\/p>\n\n\n\n

With automation-first, that cost was zero. The feature shipped in one week instead of two to three.<\/p>\n\n\n\n

\n “<\/span>\n

Imagine if I had to manually retest 15 times \u2014 each time setting up test data, checking databases, reviewing logs \u2014 each round takes 30 minutes to an hour. That’s the entire week just retesting. With automation, the developer just deploys, sees the results, and fixes. I was free to work on other things. It saves a colossal amount of time.<\/i><\/p>\n

\u2014 Victor Olkhovskyi<\/p>\n<\/div>\n\n\n\n

Why It Works for Production Bugs Too<\/h2>\n\n\n\n

The team applies the same principle to production incidents and bug fixes, but in reverse.<\/p>\n\n\n\n

When a bug is reported in production, the first step is not to fix it. The first step is to automate the failing scenario.<\/p>\n\n\n\n

This might seem counterintuitive \u2014 shouldn’t you fix the fire first? But consider the alternative: a developer pushes a fix, and then QA is asked to verify it. But QA never saw the bug firsthand. They’re testing a fixed version. How do they know they’re reproducing the exact scenario that was broken? How do they know the fix actually addressed the root cause and not just a symptom?<\/p>\n\n\n\n\n

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AUTOMATE \u2192 THEN FIX \u2192 THEN VERIFY<\/h3>\n

Step 1:<\/strong> Production bug is reported. QA writes an automated test that reproduces the failing scenario. The test runs and confirms: yes, this is broken.<\/p>\n

Step 2:<\/strong> Developer applies the fix and deploys.<\/p>\n

Step 3:<\/strong> The same test runs again. If it passes, the fix is confirmed. If it fails, the developer sees exactly what’s still wrong \u2014 no ambiguity.<\/p>\n

Ongoing:<\/strong> The test stays in the regression suite permanently. If this behavior ever regresses, it’s caught immediately.<\/p>\n<\/div>\n