Testing AI-Generated Code: A Guide for Engineering Teams

The professional acceptability of AI-assisted code generation currently hinges on the quality of your end product.

If your product remains functional and easy to use, your user base will probably not bat an eye that you are using AI for code generation. If there’s a noticeable drop in quality, expect those ‘AI slop’ allegations to roll in, and for your users’ trust in your product to plummet. Because if you’re cutting corners in this area, where else might you be doing so?

Your processes for testing AI generated code will determine which of these outcomes your team realizes. You can start generating AI code, panic at the surge in velocity, and let harmful errors through undetected. Or, you can anticipate your needs in advance, test effectively, and maintain quality.

This guide will help you achieve the latter.

You Can’t Just Let Claude Code Do Its Thing

AI tools Claude in particular are generally good at writing code. Depending on the prompt you give them, they can be very good.

However, they are not (yet?) in a place where you can guarantee that all of their output will be correct, all of the time. This is because:

• AI models make mistakes
• AI models rely on human prompts. Poor prompt = poor output

The consequences of not checking AI models’ output can be disastrous. Just ask the lawyer hit with sanctions after ChatGPT generated false case citations , or the hapless Deloitte consultant whose AI generated report lost the firm hundreds of thousands of dollars .

In the same way a lawyer should be checking their citations, or a Big 4 analyst ensuring their sources are actually real, your engineers should be testing any code generated by AI as if they had written it themselves.

AI gets it right a lot of the time. But you cannot afford reputationally or operationally to be blindly launching AI code with no checks and balances.

“But There’s Just So Much of It – We Don’t Have the Resources For Testing AI-Generated Code At Pace”

This is something that teams don’t always consider when introducing AI coding tools to their workflows. Testing has always been somewhat of a bottleneck – speeding up one end of the product development process makes no difference if the other remains clunky and inefficient.

We’re seeing the effect of this disparity right now:

• A recent CMU study suggests teams using Cursor gain a huge-but-temporary increase in development velocity, but sacrifice quality in the long run – and rack up a ton of technical debt.
• Meanwhile, both tldraw and Ghostty have limited issue creation due to the number of poor, AI-generated contributions they have received.

None of this will stop the use of AI to write code – as teams get smaller and release schedules get even more demanding, it might even be the only way of making current processes work. The differentiator will be the ability to test. Products with good testing processes will reap the benefits AI offers. Products with poor ones will shed users quickly and visibly.

Testing AI generated code effectively will involve a fundamental rethink of your testing processes. It has to – your testing processes are already inefficient. They will only become more so the more code you feed them.

Testing AI Generated Code 101: AI-Native Testing Is Essential

The answer to AI coding issues is…more AI? Sounds counterintuitive, but it’s the only way to address the issue. Here’s why.

Traditional testing processes scale poorly . Whether you’re testing everything manually or using established frameworks, more features create more tests, which create more work for your engineers. This could be due to running the tests themselves, or it could be because traditionally automated tests generate a huge maintenance burden.

Add testing AI generated code into the mix, and there’s no way you’re able to keep up.

If you shift a chunk of that work to an AI testing tool and redefine the human role as reviewer rather than tester, you can absorb that extra code development velocity. You ship code faster, with none of the trade-offs in quality.

Features You Can’t Live Without For Testing AI Generated Code

Traditionally, automated tools tie you to reactive testing – something is broken, you fix it. AI testing tools allow you to anticipate potential issues before they become a problem in the first place. This is much more efficient.

AI tools use autonomous, intelligent agents to help you do this. These understand your app as a human would, and get smarter the more you test. Here’s what this looks like in practice:

• AI-driven exploratory testing that highlights missing edge cases and high-risk areas, and suggests reasons for test failure alongside a basic pass/fail result
• Use of intent-based locators rather than brittle CSS/XPath selectors – this allows the AI to update tests after UI changes so you don’t have to spend time doing it yourself
• Natural language testing tools interpret plain English instructions into tests in seconds, so your engineers don’t waste valuable time authoring scripts

This doesn’t just allow you to test faster – it steps up the quality of your testing too. Just ask GPT Zero , who accelerated their release cycles by 80% and saw an 89% decrease in defect escape rate after implementing Momentic’s agentic AI testing tool.

Does Human Involvement Disappear?

Your engineers will still be involved in the testing process. As above – human-in-the-loop approvals are good practice for all forms of AI use.

The difference is the role they will play. Without AI testing, your engineers play the role of active tester; they author tests, patch them up after UI changes, and investigate the cause of test failures manually.

When you implement an AI tool like Momentic , your engineers’ role becomes that of an overseer. They ask the AI to create the appropriate tests using natural language prompts. They review the AI’s suggestions for test creation or updates. They parse the likely causes of failure identified by the AI, and enact them.

Without AI testing, your engineers would spend their entire workday testing AI generated code. With it, you free up hours of engineering time, with a corresponding increase in quality.

The Difference Between AI and Traditional Testing At A Glance

Testing AI Generated Code: An Example Workflow

1. Generate a feature with AI

You’ve saved hours of first-draft coding time already.

2. Run a quick human review to catch any obvious errors

All testing, AI or otherwise, is more effective when obvious code issues are resolved early.

3. Document the behavior you’re testing from a user perspective

This makes sure everyone understands what you’re testing for before you run the test. Map out critical user flows and the steps within them.

4. Create tests using a natural language tool

No coding or brittle scripts required, just clear, simple instructions – for example, ‘Verify that the user is redirected to the dashboard.’

5. Run tests in staging

This confirms whether the generated code works in a realistic environment.

6. Analyze failures

Agentic AI tools suggest reasons for test failure in order of probability – this saves a lot of time compared to starting from scratch.

7. Review other AI suggestions

Agentic AI tools can flag gaps in coverage, high-risk areas, and missing edge cases autonomously. Review any suggestions to improve quality.

8. Update code and re-run full suite

Update your code, either manually or by feeding failures back into your AI code creation tool. Then re-run the test suite to validate changes and check for any unintended side effects.

9. Deploy, then integrate tests into CI/CD

After deployment, configure tests to run automatically for any pull requests and further deployments.

Momentic: Your Go-To Choice for Testing AI Generated Code

After implementing Momentic, Coframe now catches 80% of critical UI errors before deployment, all while speeding up end-to-end test creation by 70%.

Want to join them? Get a demo today