The Testing Problem Every Developer Knows

Every developer knows the pain of testing endpoints — writing endless Postman requests, guessing headers, and hoping nothing breaks in production.

As AI reshapes how we code, why hasn't testing caught up?

The Problem: Speed vs. Safety

We're in the era of "vibe coding" — developers using AI to ship faster than ever. Startups are prototyping in days, not months. Features land in production at breakneck speed.

But there's a hidden cost.

Most teams skip testing.

We talked to over 20 engineers building API-heavy products, and the pattern was clear:

"We only test when something breaks."

"Postman is great, but keeping collections updated is a full-time job."

"By the time we write tests, the API has already changed."

Why This Happens

Manual testing tools (Postman, Insomnia, Swagger) require you to define every endpoint, write every test case, and maintain documentation manually. When your codebase evolves, you update everything. Again. And again.

Enterprise QA tools (Parasoft, Testim, Selenium) promise automation, but they're built for QA teams, not developers. They're expensive, require specialists, and take weeks to configure.

Monitoring platforms (Datadog, New Relic, Sentry) are excellent for production observability — but they're reactive, not proactive. They tell you when things break, not before you ship.

The Shift: AI That Understands Your Code

Here's what's changing in the industry:

• AI can now understand codebases end-to-end — not just syntax, but architecture.
• Automated test generation is becoming practical and intelligent.
• Continuous testing is moving left — from production back into development.

Testing should evolve at the same speed as development.

The Solution: OpenTest

That's why we built OpenTest — an AI-powered testing platform that automates the entire process.

Instead of writing scripts, developers focus on building. Here's how it works:

🧠 Automatic API Discovery

OpenTest scans your backend codebase to automatically discover every endpoint, route, and dependency. No manual configuration needed. Soon, we'll be adding frontend scanning to map the complete flow — matching endpoint calls on the frontend with their backend implementations and automatically inferring schemas across your entire stack.

✨ Auto-Generated Tests

Using AST parsing and knowledge graphs, OpenTest understands your API structure and generates comprehensive test cases for every endpoint, including edge cases you might miss.

🗺️ Visual Dependency Maps

See how your endpoints interact. OpenTest creates visual architecture maps showing data flow, route dependencies, and potential breaking points.

🔄 Auto-Healing as Your API Evolves

Your code changes? OpenTest adapts. We continuously monitor your codebase and automatically update tests as your API evolves. No maintenance overhead.

See It in Action

Here's what it looks like in practice:

Before OpenTest

After OpenTest

"We integrated OpenTest on a Tuesday. By Friday, our test coverage went from 15% to 94%."

— Early beta tester

Who This Is For

OpenTest is built for developers and teams who:

• Ship fast — Startups prototyping and iterating without dedicated QA teams
• Build solo — Independent developers who want confidence without overhead
• Move lean — Small engineering teams focused on features, not test maintenance
• Think forward — Anyone who believes testing should be as intelligent as the code it tests

Why This Matters: The Bigger Vision

We believe testing should evolve with the same speed as development.

AI can now understand APIs — it's time tools did too.

The future of software isn't just about building faster. It's about building smarter. Tools that adapt. Systems that self-heal. Testing that happens automatically, intelligently, in the background — so developers can focus on what they do best: creating.

OpenTest is the first step toward that future.

Under the Hood

For the technically curious, here's what powers OpenTest:

• Backend: Node.js for speed and flexibility
• Frontend: Next.js for modern developer experience
• AI Models: Currently using DeepSeek 3.2 for cost-effective testing and inference. Teams can configure their preferred AI models based on their needs and budget.
• Architecture Intelligence: AST parsing + knowledge graphs for deep code understanding
• Database: PostgreSQL for reliable data management
• Hosting: Railway for development and testing environments, with AWS exploration underway for production-grade robustness and scalability