Testing AI-Generated Code: What QA Engineers Need to Know

Your developers are shipping code faster than ever. GitHub Copilot, Cursor, Claude Code. AI writes the first draft, the developer reviews, and it goes into the codebase. The problem? Recent research shows that over half of AI-generated code samples contain logical or security flaws. The code compiles. It passes a quick read. But it breaks in ways that human-written code typically does not. As a QA engineer, this changes what you test and how you test it.

How AI-Generated Bugs Are Different

Human developers make predictable mistakes. They forget edge cases they have not encountered. They copy-paste and miss a variable name. They rush under deadline pressure. AI makes a different category of mistakes:

Confident but wrong logic. AI generates code that looks correct at first glance but contains subtle logical errors. A sorting function that works for most inputs but fails on empty arrays. A date calculation that is off by one day in certain timezones.

// AI-generated: looks correct, has a bug
function calculateAge(birthDate: string): number {
  const birth = new Date(birthDate);
  const today = new Date();
  const age = today.getFullYear() - birth.getFullYear();
  return age;
}
 
// Bug: does not account for whether the birthday
// has occurred yet this year.
// Born Dec 15, 2000. Today is March 1, 2026.
// Returns 26, actual age is 25.

Security blindspots. AI often generates code that works functionally but ignores security best practices. SQL queries without parameterization. User input rendered without sanitization. API keys hardcoded in client-side code.

# AI-generated: works but vulnerable to SQL injection
def get_user(username):
    query = f"SELECT * FROM users WHERE name = '{username}'"
    return db.execute(query)
 
# What it should be:
def get_user(username):
    query = "SELECT * FROM users WHERE name = %s"
    return db.execute(query, (username,))

Hallucinated APIs. AI sometimes calls functions or methods that do not exist. It confidently uses a library API that was in its training data but has since been deprecated or never existed in the version you are running. Missing error handling. AI generates the happy path well. It often skips error handling, retry logic, timeout management, and cleanup code.

What to Test Differently

When you know a feature was built with AI assistance, adjust your testing focus:

1. Boundary conditions get extra attention

AI is particularly weak at boundaries. Test:

• Empty inputs, null values, undefined
• Maximum and minimum values
• Zero, negative numbers, very large numbers
• Empty arrays, single-element arrays
• Strings with special characters, unicode, emoji

Test: Shopping cart total calculation
Input: Cart with 0 items
Expected: $0.00, not NaN, not an error
 
Input: Cart with 1 item at $0.00 (free sample)
Expected: $0.00 total, not skipped
 
Input: Cart with item quantity of 999999
Expected: Correct total, no overflow

2. Security testing becomes mandatory

For every AI-assisted feature, check:

• Input validation. Does it sanitize user input? Try XSS payloads, SQL injection strings, path traversal attempts.
• Authentication. Can you access the endpoint without a token? With an expired token?
• Authorization. Can User A access User B's data?

Test: User profile API
Input: GET /api/users/123 with User B's auth token
Expected: 403 Forbidden
Actual (AI bug): 200 OK with User A's data

3. Error paths need explicit testing

AI generates working happy paths. Your job is to break them:

• What happens when the API returns a 500?
• What if the database connection drops mid-transaction?
• What if the third-party service times out?
• What if the user submits the form twice rapidly?

4. Integration points are high risk

AI writes individual functions well. It struggles with how those functions interact with the rest of the system. Test the boundaries between components:

• Does the frontend correctly handle every response format the API returns?
• Are database transactions committed and rolled back correctly?
• Do event handlers fire in the correct order?

Practical Testing Checklist for AI-Assisted Features

How to Talk About This With Your Team

This is not about blaming AI or the developers who use it. AI assistance makes developers faster. But faster code production without proportionally better testing means more bugs in production. As a QA engineer, your value just increased. You are the safety net for a development process that is moving faster than ever. Position yourself as the person who makes AI-assisted development actually work by catching what the AI misses.

When you find an AI-generated bug, do not just file it. Note the pattern. Was it a boundary issue? Security? Error handling? Over time, you build a mental model of where AI tends to fail, and you can proactively test those areas on every feature. The teams that ship quality software with AI assistance are the ones where QA is involved early, testing is thorough on the areas AI gets wrong, and the feedback loop between QA and development is tight. AI writes code fast. Your job is to make sure it works.