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AI-generated code needs tests that define what “working” means. Test-driven development (TDD) provides a practical way to do that: write an automated test for a behavior, confirm it fails, implement enough code to pass, then refactor and repeat. Tests give a coding machine an executable target—but only for the behaviors the tests actually check.
Why machine-generated code makes TDD more important
In a LinkedIn excerpt describing his Communications of the ACM article, Abtin Aghagolian argues that a test can serve as an executable interface for machine-written code. A natural-language prompt can leave room for interpretation; an automated test can be run against the implementation and return a concrete pass or failure. Aghagolian puts the idea this way: “When a machine writes the implementation, the test stops being a discipline and becomes the interface.” Read Aghagolian’s post.
That is an argument about how to guide code generation, not proof that every coding task requires TDD or that tests guarantee correctness. A passing result establishes only that the implementation met the checks that were written. If those checks are weak, incomplete, or aimed at the wrong behavior, generated code can pass while still being flawed.
What TDD means in practice
TDD uses a short, repeated feedback cycle, rather than postponing all testing until after implementation. The cycle is:
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- Write a test for one intended behavior. Make the test specific enough that its result has a clear meaning.
- Run it and confirm it fails. This checks that the test can detect the missing behavior instead of passing regardless of the implementation.
- Implement the minimum needed to pass. For machine-generated code, the test becomes a concrete target for the implementation.
- Refactor and repeat. Clean up the code while keeping the test suite passing, then add a test for the next behavior.
This is the red–green–refactor rhythm described in Succeeding with Agile: identify and automate a failing test, write just enough code to make it pass, then clean up before the next cycle. Its contrast is with code-first work that discovers problems through compile fixes and debugging after implementation. See the book description.
What a test can—and cannot—specify for an AI assistant
A test turns a requirement into something executable, but it does not automatically capture the entire requirement. A useful test suite checks intended behavior, relevant edge cases, and interactions among behaviors. Tests that merely mirror the current implementation or check a narrow happy path can leave important requirements uncovered.
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- Behavior: Check the result a user or another system should observe, not just an internal implementation detail.
- Boundaries: Include meaningful invalid inputs, empty values, limits, or error conditions where they matter to the feature.
- Interactions: Check whether behaviors still work together. Separate tests can pass individually without showing that a combined response is coherent.
- Repeatability: A failure should be reproducible and informative enough to help diagnose what the implementation did wrong.
Aghagolian’s excerpt raises the concern that tests for individual behaviors may not express whether a combined response is coherent. That is a useful caution, but the available excerpt does not establish a specific example or a general solution. Treat the suite as one source of evidence about the implementation, not as a complete definition of correctness.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesDoes the “25 years” claim describe TDD adoption?
The phrase “Nobody Did TDD for 25 Years” works as a provocation, not as an established industry-wide statistic. The available material does not provide an independently verified study showing how many developers used TDD over that period. Historical figures sometimes repeated in older summaries—including a reported 15% increase in development time and reported bug reductions in two Microsoft studies—are secondhand here; the original studies were not checked, so they should not be treated as verified findings.
The defensible takeaway is narrower: TDD offers a disciplined way to give machine-generated code an executable target, and the quality of that target depends on the quality and scope of the tests. It does not follow that nobody used TDD, that AI makes TDD universally mandatory, or that passing tests prove software is free of defects.
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