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Improving Test Coverage with AI

AI can help you move from low coverage to meaningful coverage quickly. The goal isn't to hit a percentage; it's to cover the scenarios that matter.

Find uncovered paths​

Run your coverage tool and paste the report or the uncovered code:

Here is my coverage report. These lines are uncovered:

src/utils/permissions.ts: lines 34-41, 67-72

Here's the file: [paste]

Write tests that cover the uncovered branches.

Identify high-risk untested code​

Rather than chasing 100% coverage mechanically, ask the model to prioritise:

Here are the files with the lowest test coverage in our codebase:

[list files]

Which of these are highest risk if they contain bugs? Rank them and explain why.

Generate property-based tests​

For pure functions, property-based testing finds edge cases no one thought of:

Write property-based tests for this sorting function using fast-check.
Define properties that should hold for any valid input array.

[paste function]

Fill coverage gaps systematically​

Work file by file. For each uncovered area:

  1. Ask the model to explain what the code does
  2. Ask it to list all possible paths through the code
  3. Ask it to write a test for each path
Explain what each branch of this switch statement does,
then write one test per branch.

[paste code]

Coverage anti-patterns to avoid​

When using AI for coverage, watch out for:

  • Tests that only assert the call happened, not that the result is correct
  • Mocking everything: tests that mock so much they don't test real behaviour
  • Duplicate tests: AI sometimes generates near-identical tests for the same path
  • Tests with no assertions: always verify there's at least one expect() per test