Effective regression testing in the age of AI-generated code

AI code generation is rapidly evolving from a novelty into a key building block of modern software development.

According to the Tricentis 2025 Quality Transformation Report, 82% of software professionals are excited about AI agents handling repetitive development tasks, and 84% believe AI will help teams meet increasingly compressed deadlines. Tools like GitHub Copilot and Codex are driving this revolution, offering real-time suggestions and automating boilerplate work.

But teams will have to learn to automate tests on code that is increasingly generated by AI, not humans. Companies like Microsoft and Google have reported that between 25-30% of their code is AI-generated, and Anthropic’s CEO has predicted that within a year, AI could be writing essentially all code.

Here’s the fundamental problem: The regression testing strategies that worked for human-written code are breaking down under the unique characteristics of AI-generated code.

When AI-generated code goes wrong

Agentic AI can generate and make changes to code much faster than a developer can, but prompting a code writing agent isn’t as simple as having it write a new line of code. Often, a lot of the existing codebase changes every time you tell your agent to add something to it.

The constant changes can mean that AI-generated code is not always reliable. According to a recent report from Harness, more than two-thirds (67%) of developers reported that they spend more time debugging AI-generated code.

But without proper testing guardrails, AI-generated code can cause business-critical failures that destroy data, disrupt operations, and erode trust.

For example, in July 2025, Replit’s AI agent deleted a tech investor’s entire production database despite explicit freeze instructions, admitting it had “panicked” and executed unauthorized commands that destroyed months of work. The AI then falsely claimed the data was unrecoverable.

This pattern of ignoring instructions and making unauthorized changes highlights how AI agents can operate dangerously outside expected boundaries. The company immediately implemented new safeguards, but the lesson here is that AI makes architectural decisions and modifies critical systems in ways that a human developer wouldn’t. This has serious implications for testing teams.

Traditional testing approaches assume contained changes, but AI coding agents operate fundamentally differently, relying on pattern recognition and operating within a limited context. The result is that they often generate unconventional or inefficient code outputs that work in isolation but create problems within larger, complex systems.

Traditional regression testing wasn’t built for this

Traditional regression testing strategies rely on a fundamental assumption that most of the codebase remains stable between releases. Test selection algorithms identify “areas of risk” based on what changed according to the requirements. If you’re adding login features, you need to run tests for authentication, session management, and user flows. You trust that other parts of the system don’t change because no one was working on them.

But this assumption collapses with AI-generated code.

When an AI agent changes code, a simple login feature might have prompted the AI to refactor a shared function that’s called by lots of different modules. It might have updated a database helper that the payment system depends on, for instance. It might have changed error-handling patterns that affect the admin dashboard. None of these changes were in the requirements, and none of them are obvious from the feature description, but all of them need to be tested now.

Traditional regression test selection can miss what actually changed, and that gap is where the AI-generated bugs will hide.

What modern regression testing needs to do differently

Modern regression testing needs to intelligently identify what code has changed, and not just what was supposed to change according to the requirements. It should track the ripple effects that AI modifications create throughout the codebase: the refactored utilities, the updated dependencies, and modified shared functions that touch dozens of modules.

Here’s what that shift should look like:

• From requirement based to change-based test selection: In addition to asking “what feature are we adding?” you should also ask “what code actually changed, and what depends on it?”
• From periodic regression suites to continuous change detection: AI generates code constantly. Testing can’t wait for release cycles — it needs to happen in real time as changes happen.
• From trusting architectural boundaries to verifying them: Developers respect module separation, but AI doesn’t. Every change needs dependency analysis to understand the full scope of impact.
• From manual test selection to automated risk assessment: When AI touches 10 files for a single feature, humans can’t manually identify all the affected tests. The system needs to map code changes automatically.

Solutions like Tricentis SeaLights address this challenge by blocking untested code changes from reaching production and intelligently executing the tests relevant to what has changed — not what was planned to change. This change-based approach allows teams to maintain the speed advantages of AI code generation while preventing the quality risks that come with it.

By automatically identifying which code has been modified and which tests cover those modifications, these tools provide the visibility and control that traditional regression testing frameworks can’t offer in an AI-driven development environment. The gap between how AI generates code and how we test it is real, measurable, and growing. Organizations that invest in regression testing approaches designed for AI’s unique behaviors will capture the productivity benefits of AI code generation without sacrificing quality. Those that don’t may find themselves caught in the testing debt spiral: untested AI code creating more bugs, requiring more fixes, and generating more untested code.

The regression testing approaches that worked for human developers served us well for decades. But AI is changing the game, and it’s time our testing strategies caught up.