Building PatchFlow for the QwenCloud Hackathon

Jason Yakubu — Wed, 24 Jun 2026 10:19:30 +0000

Over the past few days, I've been building PatchFlow, an experiment in autonomous API reliability.
The idea started from a frustration I've experienced while building backend systems.
When an API fails, the workflow is often the same:

Reproduce the issue
Search logs
Trace the failure
Find the relevant code
Create a fix
Open a pull request Even with modern tooling, much of this process remains manual. I wanted to explore whether some of these steps could be automated.

The Idea
PatchFlow is designed to help developers move from:
Issue → Root Cause → Patch
The goal is not simply to detect failures but to assist with understanding and fixing them.
At a high level, the workflow looks like this:
Plain text
OpenAPI Specification
↓
Endpoint Discovery
↓
Reliability Testing
↓
Failure Analysis
↓
Root Cause Detection
↓
Patch Generation
↓
GitHub Pull Request

Early Challenges
One of the first technical challenges I encountered was endpoint discovery.
My initial idea was to scan repositories and automatically discover API routes regardless of framework.
The more I explored this approach, the more limitations I found.
Different frameworks expose routes differently:
FastAPI
Django
Express
Next.js
Spring Boot
Attempting to support every framework through repository scanning quickly became complex and inefficient.
Why I Switched to OpenAPI
After researching how existing API platforms operate, I realized that many tools rely on OpenAPI specifications rather than trying to discover endpoints from source code.
OpenAPI already provides:

Endpoint definitions
Request schemas
Response schemas
Methods and parameters

This allows PatchFlow to focus on reliability testing instead of route discovery.
The architecture immediately became cleaner and more scalable.

Building with QwenCloud
For this project, I'm using QwenCloud to power the reasoning and analysis components of the platform.
The ability to work through architectural tradeoffs, evaluate different approaches, and iterate quickly has been particularly valuable during development.
One example was evaluating multiple approaches for endpoint discovery before settling on an OpenAPI-first workflow.
That decision significantly simplified the overall system design.
Current Progress
So far I have:

✅ Defined the product architecture
✅ Chosen an OpenAPI-first testing workflow
✅ Planned GitHub integration for automated pull requests
✅ Started frontend development
Next, I'll focus on:

Reliability testing engine
Failure analysis workflows
GitHub patch generation
End-to-end demo preparation

What I'm Learning

One lesson from this project is that many successful developer tools are effective because they reduce complexity rather than add more intelligence
.
My original design attempted to automatically discover every endpoint in every framework.
The current design is simpler, more practical, and closer to how developers already work.
Sometimes the best solution is not the most automated one.
Looking Ahead
The goal for the remainder of the hackathon is to build a working prototype that can:

Read an OpenAPI specification
Test critical endpoints
Identify failures
Suggest a fix
Generate a GitHub pull request

I'll continue sharing updates as PatchFlow evolves

Why APIs Break in Production and Why We Still Test Them Manually

Jason Yakubu — Tue, 16 Jun 2026 07:52:55 +0000

Modern software is built on APIs.

Every login, every payment, every user action — it all flows through backend APIs. And yet, despite how critical they are, API reliability is still surprisingly fragile.

We still rely on manual testing tools, handwritten scripts, and fragmented debugging workflows to ensure systems don’t fail in production.

And somehow, that hasn’t changed in years.

The Hidden Reality of Backend Systems

On the surface, APIs look stable:

They have defined routes
They return structured responses
They pass unit tests
They work in staging

But in real-world usage, things break in ways that are not always obvious:

A missing null check crashes a login flow only under specific inputs
A slow database query causes intermittent checkout failures
A malformed payload passes validation in staging but fails in production
A refactored service silently breaks a dependent endpoint
A race condition only appears under load

These are not rare edge cases — they are everyday production issues.

And the worst part is:

Most of them are discovered by users, not developers.

Why Current API Testing Is Not Enough

Today’s API testing ecosystem is powerful, but fundamentally limited in scope.

Postman and manual testing

Postman is widely used, but it relies on manually defined requests.

You must already know:

Which endpoint exists
What payload it expects
What scenarios to test

It is a tool for executing tests, not discovering system weaknesses.

CI-based test suites

Automated tests in CI pipelines help catch regressions, but they suffer from one core limitation:

They only test what developers explicitly wrote.

If a scenario was never imagined, it is never tested.

Observability tools (Datadog, etc.)

Monitoring tools are excellent at telling you what is broken in production.

But they operate after the fact.

They answer:

“What failed?”
“Where did it fail?”

But not:

“Why did this happen in the first place?”
“Could this have been prevented?”
“How do we fix it automatically?”
The Gap Between Testing and Fixing

Across the entire software lifecycle, there is a missing layer:

We test APIs manually or partially automatically
We observe failures in production
We debug and fix issues manually

But there is no system that closes the loop.

There is no system that:

Understands backend APIs, tests them intelligently, identifies failures, and fixes them automatically.

That gap is where most production bugs live.

A Different Approach: Treat APIs as Systems, Not Endpoints

The real problem is not testing individual endpoints.

The real problem is understanding how the system behaves under failure.

Instead of asking:

“Does this endpoint work?”

We should be asking:

“What happens when this system is stressed, misused, or given unexpected input?”

This requires moving beyond manual test cases and static assumptions.

It requires an autonomous approach.

Toward Autonomous API Reliability

A more complete system would work like this:

Understand the API surface

Instead of manually defining tests, the system reads structured API definitions (such as OpenAPI specifications) or derives them from the codebase.

Generate intelligent test scenarios

It does not just test “happy paths”, but also:

invalid inputs
missing fields
type mismatches
unexpected payload structures
failure conditions under stress

Simulate real-world failures

It intentionally breaks assumptions:

slow responses
partial failures
incorrect sequencing of requests

Analyze and trace failures

When something breaks, it does not stop at reporting an error. It traces the failure back to the likely source in the codebase.

Propose and apply fixes

Instead of stopping at detection, it generates code patches and opens pull requests for review.

This closes the loop between:

detection → diagnosis → correction

Why This Matters

Software complexity is increasing faster than our ability to manually test it.

Modern backend systems are:

distributed
event-driven
dependent on multiple services
deployed continuously

In such environments, manual testing does not scale.

Even traditional automation struggles because it assumes we can predict all failure cases in advance.

But real systems don’t fail in predictable ways.

The Direction Forward

The future of backend reliability is not more dashboards.

It is not more manual test scripts.

It is systems that can:

understand APIs automatically
stress-test intelligently
identify root causes
and propose fixes in real time

In other words:

Software that participates in its own debugging process.

Closing Thought

We have spent years building better tools to write code faster.

Now the next step is clear:

We need tools that help us trust code more, not just write it faster.

Because in production systems, speed of development means nothing without reliability.

And reliability should not be entirely manual anymore.

DEV Community: Jason Yakubu

Building PatchFlow for the QwenCloud Hackathon

Why APIs Break in Production and Why We Still Test Them Manually