QA for vibe coding: how to test AI-generated code

What is vibe coding?

Vibe coding is a software development approach where you describe what you want to an AI assistant, and it generates the code for you. The term was coined by Andrej Karpathy, founding team member of OpenAI, in early 2025.

Instead of writing every line by hand, vibe coders guide AI tools like Cursor, GitHub Copilot, or Claude Code with natural language prompts. You describe the "vibe" of what you want, and the AI handles the implementation details.

This approach has exploded in popularity. According to Qodo's State of AI Code Quality 2025 report, 82% of developers now use AI coding assistants daily or weekly, and 41% of all code is AI-generated or AI-assisted.

But here's the problem: traditional QA processes weren't designed for this new reality.

Why traditional QA fails with AI-generated code

The core issue isn't that AI writes bad code—it's that vibe coding fundamentally changes the development workflow in ways that break traditional QA models.

Testing can't keep pace

Google's 2024 DORA report found a surprising result: software delivery actually slowed down despite AI productivity gains. The culprit? Testing couldn't keep up.

When developers can generate code 10x faster, QA becomes an instant bottleneck. Around 70-80% of organizations still rely primarily on manual testing methods, which simply can't scale with AI-accelerated development.

Hidden assumptions and logic flaws

When you write code yourself, you understand every decision. With vibe coding, the AI makes thousands of micro-decisions you never see.

Research from Arxiv examining vibe coding practices found that the casual approach inherent to vibe coding often allows dangerous assumptions to go unnoticed in generated implementations.

Security blind spots at scale

According to Veracode's 2025 GenAI Code Security report, nearly half (45%) of all AI-generated code contains security flaws, despite appearing production-ready.

Common vulnerabilities include:

• Injection flaws: Code generated without proper input sanitization
• Broken authentication: AI may omit access controls entirely
• Hardcoded secrets: Credentials embedded directly in code
• Insecure dependencies: Outdated or vulnerable packages suggested

The problem compounds because vibe coding's speed-first culture often bypasses traditional security gates like static scanning.

The 5 pillars of vibe coding QA

Building effective QA for vibe coding requires a fundamentally different approach. Here are the five pillars that successful teams implement.

1. Treat AI output as draft code

The most critical mindset shift: never ship AI-generated code without review.

Think of AI output like a junior developer's first draft—it might be 80% correct, but the remaining 20% could contain critical bugs or security issues. According to Qodo's research, 75% of developers still manually review every AI-generated code snippet before merging.

This isn't a workflow bug to fix—it's the correct approach. The most successful vibe coders view AI as an assistant that accelerates their work, not a replacement for their judgment.

2. Mandatory code review for all AI output

Traditional code review asks: "Did the developer make any mistakes?"

Vibe coding review asks: "Did the AI understand the requirements correctly?"

This is a fundamentally different question. You're not just checking for typos—you're validating that the AI interpreted your intent correctly and didn't make hidden assumptions.

Effective vibe coding reviews should check:

• Does the code do what was requested (not just what was prompted)?
• Are there edge cases the AI didn't consider?
• Does it follow your project's conventions and patterns?
• Are there any security implications?

3. Security scanning on every change

Since vibe coding often bypasses pre-production security tools, you need automated guardrails that can't be skipped.

Implement:

• SAST (Static Analysis): Run tools like SonarQube or Semgrep on every PR
• Dependency scanning: Check for vulnerable packages automatically
• Secret detection: Scan for accidentally committed credentials
• DAST (Dynamic Analysis): Regular penetration testing for deployed code

The key is automation. Manual security reviews can't scale with vibe coding velocity.

4. Test-driven development integration

TDD becomes even more powerful with AI code generation. When you write tests first, they act as precise specifications that guide the AI toward exactly the behavior you expect.

The workflow looks like this:

1. Red: Write a failing test that specifies what you want
2. Green: Let AI generate code to pass the test
3. Refactor: Have AI clean up the implementation while keeping tests green

This approach has a crucial benefit: it prevents AI from determining both "what" the code should do and "how" it should do it. You control the "what" through tests; AI handles the "how."

Research from Qodo shows that TDD provides guardrails that dramatically improve AI-generated code quality by defining expectations first.

5. Runtime monitoring

Since vibe coding can create vulnerabilities that slip past pre-production testing, runtime security monitoring is no longer optional.

Implement:

• Application performance monitoring (APM): Track behavior anomalies
• Error tracking: Catch unexpected failures in production
• Security monitoring: Detect potential attacks against vulnerabilities
• Log analysis: Identify unusual patterns that might indicate bugs

Continuous monitoring of live applications is the only way to gain visibility into how AI-generated code actually behaves and to detect attacks against vulnerabilities that were unaddressed during development.

Building a vibe coding QA workflow

Here's a practical workflow that combines all five pillars:

Before writing code

1. Write acceptance criteria that clearly define expected behavior
2. Create tests first (unit tests, integration tests) that encode requirements
3. Define security requirements for sensitive features

During development

1. Generate code with AI using your tests as guardrails
2. Run tests continuously as you iterate
3. Use AI to help write additional tests for edge cases
4. Run SAST/security scans on each change

Before merging

1. Self-review all AI output against original requirements
2. Peer review focusing on intent validation
3. Automated CI checks (tests, security, linting)
4. Check test coverage meets your threshold

After deployment

1. Monitor error rates and performance
2. Track security alerts
3. Review production logs for anomalies
4. Schedule periodic security assessments

How AI QA Live Sessions fits the vibe coding workflow

Traditional test automation requires writing and maintaining test scripts—which becomes another bottleneck when you're generating code at vibe coding speeds.

AI QA Live Sessions takes a different approach: you describe what needs testing in natural language (just like you describe code to generate), and AI handles the test execution. Watch live as AI navigates your application, validates functionality against your acceptance criteria, and generates detailed bug reports with recordings.

This creates a natural pairing with vibe coding:

• Same mental model: Describe intent in natural language, AI handles execution
• Matching velocity: Test generation keeps pace with code generation
• Visual validation: See exactly how your AI-generated code behaves
• No maintenance burden: No test scripts to update when code changes

Frequently asked questions

Is vibe coding safe for production applications?

Yes, with proper guardrails. The key is implementing robust QA processes designed for AI-generated code. Companies using structured approaches—code review, automated security scanning, TDD, and monitoring—successfully ship vibe-coded applications to production.

How much AI-generated code should be reviewed?

All of it. According to industry data, 75% of developers manually review every AI-generated snippet. This isn't excessive—it's the minimum standard for production code. The review doesn't need to be exhaustive for every line, but someone should validate that the AI understood the intent correctly.

Can AI tools help with QA for AI-generated code?

Absolutely. AI-powered testing tools can help generate test cases, identify edge cases, and automate validation. The key is using AI as an accelerator for your QA process, not as a replacement for human judgment on critical decisions.

What's the biggest mistake teams make with vibe coding QA?

Treating AI-generated code the same as human-written code. The testing approaches that worked when developers wrote every line don't automatically apply when AI generates most of the code. Teams need to consciously adapt their QA processes for the new reality.

How do I convince my team to invest in vibe coding QA?

Point to the data: 45% of AI-generated code contains security flaws, and delivery is slowing despite AI productivity gains because testing can't keep pace. The choice isn't whether to invest in vibe coding QA—it's whether to do it proactively or after an incident.

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Vibe coding isn't going away—if anything, it's accelerating. The teams that thrive will be those who build QA processes designed for AI-first development, not those trying to retrofit traditional approaches onto a fundamentally different workflow.