Q11 of 21 · AI for testing

How do you use AI to improve test coverage without generating hundreds of redundant tests?

AI for testingMidai-for-testingtest-coveragecoverage-gapai-toolstest-generation

Short answer

Short answer: Use AI to analyse your existing coverage reports and identify untested paths, then generate targeted tests for those specific gaps — not a broad sweep. Feed the coverage data as context so the model focuses on uncovered branches rather than re-covering happy paths.

Detail

The failure mode of AI-assisted coverage is generation without focus: given "write more tests," a model will produce more tests for the already-well-covered paths because those are the patterns it recognises from your existing suite.

A better workflow:

  1. Run coverage analysis and identify specific untested branches or uncovered API paths.
  2. Provide the coverage report and the specific uncovered area to the model: "This branch of the payment handler (lines 142–158) is untested. Write a test that exercises the timeout path."
  3. Review the generated test against the actual code to verify the assertion is meaningful.

For intelligent test selection — choosing which existing tests to run rather than which new ones to generate — see Coverage prioritisation and Intelligent test selection. These are distinct use cases that AI also supports.

// WHAT INTERVIEWERS LOOK FOR

Targeted generation (coverage-driven, not broad sweep). The failure mode of generating more tests for already-covered paths. Distinction between test generation and intelligent test selection.