AI Has No Real Understanding of What’s “Correct”

AI-assisted code generation has reshaped how organizations produce software. This article examines how this shift affects software quality. The central issue is that AI-generated code is often produced by tools that do not understand the broader context of the application infrastructure or the process being automated. Using functional tests as a baseline can help reduce these risks.

Everyone recognizes that AI “speeds” up development. Recent research found that developers using AI tools increased their coding activity, measured by commits, by as much as 180%. However, writing more code does not automatically lead to better software: AI-generated code frequently introduces defects that developers then have to resolve.

AI-generated code often pushes developers into rework and debugging for code that “only took a few seconds” to produce. What AI still lacks to reliably generate and ship correct code is a written, testable record of how the product is supposed to behave. Much of this context is scattered across disconnected tools or stored as internal knowledge, forcing AI to infer a developer’s intent. This is where test artifacts support AI-assisted development. Test context captures detailed user flows in step-by-step form. Unlike requirements or other development artifacts that represent a snapshot of how features were once implemented, tests reflect the most accurate expected behavior of the product. A regression suite consolidates the cumulative and current expectations of product functionality, including the many edge cases, data variations, and environments the system must handle in real-world use.

Rather than treating tests as a final validation gate, software development leaders can gain more by adopting a tests-as-context approach, where functional tests provide a behavioral baseline that AI can build against. A more integrated approach to software quality helps organizations move from flawed AI-generated code toward AI that can build software in a near-autonomous way.

The Debugging Loop Nobody Wanted

Today’s AI software development lifecycle (SDLC) enables AI to start writing code for a new feature automatically, often without the context needed to preserve existing behavior. As a result, it may implement the new feature while causing a regression in an older one. This creates a cycle where testers log defects and developers must pause other work to fix and revalidate them.

Consider an e-commerce team preparing a promotional feature ahead of Cyber Monday. They use AI to help implement a “buy one, get one 50% off” promotion, but the AI assistant does not know the existing business rules for the product. Without that context from the test suite, customers may be able to stack discounts, bypass gift card restrictions, and earn unauthorized loyalty points, leading to significant revenue loss. None of these outcomes were part of the requested feature, but the intent to prevent them was embedded in existing tests.

While this example centers on feature delivery, the same absence of behavioral context creates challenges across testing workflows:

As testing becomes increasingly impacted by AI limitations, more defects reach production. One report found that 15% of commits from AI coding assistants introduced at least one issue, and 22.7% of those issues persisted into the latest version of the repository. Addressing these problems requires shifting testing left and providing AI with the context it needs from the beginning.

How to Fix the AI SDLC

To shift testing left and start repairing the AI SDLC, software development leaders can implement a few steps:

Taken together, these steps move testing from an end-stage checkpoint to a behavioral context layer that guides AI-assisted development. This approach helps enterprises move beyond static specifications to a living, evidence-based record of how the system behaves under real execution conditions. It also makes execution evidence a basis for evaluating the real impact of AI across software, ultimately reducing escaped defects and limiting time spent on rework.

A Better Way to Build with AI

Across the industry, the common response to AI coding defects is to generate new tests from natural language and run regression tests on AI-produced code after the fact. The tests-as-context approach enables software developers to prevent AI-generated code from breaking behavior in the first place.

When organizations make this shift, they establish a shared framework that supports confidence, visibility, and control as AI accelerates software development.