Martin Fowler proposes writing tests as data to keep AI-generated code verifiable
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Martin Fowler published an article proposing that tests be written as data rather than code, so that AI-generated production code stays verifiable even when coding agents also refactor the tests. The method combines customer tests, black-box testing, tabular or YAML test cases and an AI-generated test runner, with test data kept in text files that are easier to review and protected from unexpected edits. The proposal draws on interviews with 26 Thoughtworks developers in early 2026 who said they no longer fully trust their tests once AI refactors them. Fowler pairs the method with mutation testing, property-based tests and human exploratory testing.
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- Hacker News · AITests as data: a method for keeping AI-generated code verifiable
Martin Fowler proposes writing tests as data rather than code so that AI-generated production code stays verifiable when coding agents also refactor the tests. The approach combines customer tests, black-box testing, tabular or YAML test cases, and an AI-generated test runner, with the test data kept in text files that are easier to review and protect from unexpected edits. The article draws on interviews with 26 Thoughtworks developers in early 2026 who said they no longer fully trust their tests once AI refactors them, and it pairs the method with mutation testing, property-based tests, and human exploratory testing.
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