AI Testing

Explanation

AI tools excel at generating test boilerplate and suggesting edge cases — tasks developers often skip under time pressure. Given a function, an LLM can generate a comprehensive test suite covering happy path, error cases, boundaries, and null inputs in seconds. This lowers the activation energy for writing tests. Effective patterns: 'generate unit tests for this function' (provide the function, get test cases), 'what edge cases am I missing?' (show your tests, get gaps identified), 'write a test that proves this bug is fixed' (describe the bug, get a regression test), and 'mock this dependency' (provide the interface, get a test double). Limitations: AI generates tests based on the code it's given — if the implementation has a bug, tests may codify that bug as correct behavior. Tests for the happy path often miss adversarial inputs that matter most for security. AI cannot identify untested behavior it can't see. Performance and integration tests require environmental context AI can't access. The fundamental testing principle still holds: tests verify specifications, not implementations. If your specification is 'make the AI-generated tests pass,' you're testing nothing meaningful. Define required behavior first, then generate tests for that behavior.

Code Example

// AI-generated test (Jest) — prompt: 'Generate comprehensive tests'

import { calculateDiscount } from './discount';

describe('calculateDiscount', () => {
  // Happy path
  it('applies 10% for SAVE10 coupon', () => {
    expect(calculateDiscount(100, 'SAVE10')).toBe(90);
  });

  // Edge cases AI suggested:
  it('returns original price for invalid coupon', () => {
    expect(calculateDiscount(100, 'INVALID')).toBe(100);
  });

  it('handles zero price', () => {
    expect(calculateDiscount(0, 'SAVE10')).toBe(0);
  });

  it('result is never negative', () => {
    expect(calculateDiscount(5, 'SAVE90')).toBeGreaterThanOrEqual(0);
  });

  it('throws for negative price', () => {
    expect(() => calculateDiscount(-10, 'SAVE10'))
      .toThrow('Price cannot be negative');
  });
});

// HOW TO VERIFY AI-GENERATED TESTS ARE MEANINGFUL:
// Temporarily break the implementation:
// const calculateDiscount = (price) => price; // always returns price unchanged
// Every test above should now FAIL — if any still pass, that test is testing nothing

Why It Matters for Engineers

Testing is one of the highest-leverage skills for production-quality software, and one of the areas vibe coders skip most often. AI testing tools lower the cost of writing tests dramatically. Use them. Understanding AI testing's limitations prevents false security: a passing AI-generated test suite means code passes the tests the AI thought to write, not that code is correct. Verifying AI-generated tests against the actual specification — and confirming they fail when the implementation is broken — is the meaningful skill.