AI-generated code needs tests that define the behavior it must meet—not just a prompt describing what to build. Test-driven development (TDD) makes those checks part of the coding loop: write a failing test, implement enough code to pass it, then refactor. That gives a coding machine an executable target, but it does not guarantee correct software; the tests must cover the behavior that matters.
What TDD means in practice
TDD is a repeated, small-scale feedback cycle:
- Write a test for a behavior. The test should fail because the behavior is not implemented yet.
- Write the smallest implementation that passes. Use the failure to guide the next change.
- Refactor. Improve the code while keeping the tests passing, then repeat for the next behavior.
This differs from writing a large block of code first and relying on later compilation and debugging to discover problems. In TDD, an automated check provides feedback at each step. The description of this cycle in Succeeding with Agile is available in its excerpt: TDD excerpt.
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Why machine-written code changes the role of a test
Abtin Aghagolian’s excerpt about his Communications of the ACM article frames a test as an executable interface for a code-writing machine. A natural-language prompt can leave room for interpretation; a test runs and produces a checkable result. His post puts the idea this way: “When a machine writes the implementation, the test stops being a discipline and becomes the interface.” Aghagolian’s LinkedIn excerpt
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That is an argument about how to guide machine-generated implementation, not proof that tests are always clearer than prompts or that TDD is required for every task. A test only specifies the cases and outcomes its author encoded. If the expected behavior is vague, or important cases are missing, the machine can satisfy the check while producing a flawed result.
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What tests can—and cannot—establish
A passing test shows that the code met the checks that were written. It does not establish that the checks are complete, that the design is sound, or that the software is free of defects. This limitation matters with AI-assisted coding: the assistant may optimize for the visible test while overlooking an unstated requirement.
Tests can also check behaviors individually without demonstrating that their combination makes sense. A feature may pass its isolated checks yet produce an incoherent response when those behaviors interact. The available excerpt raises this concern, but the full example and its context are not accessible, so it should be treated as a question for test design rather than a confirmed universal failure mode.
How to make TDD useful with an AI coding assistant
Use tests to express observable requirements, then inspect whether the test suite covers the cases and interactions that matter. A practical review can ask:
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- Are important edge cases represented? Consider invalid inputs, empty results, boundary values, and failure paths when they are relevant to the feature.
- Do behaviors work together? Add integration-level checks where separate passing behaviors could still combine badly.
- Can failures be reproduced and understood? Clear, focused tests make it easier to identify whether a change broke a requirement.
- Is the result reviewed beyond the tests? Passing checks are evidence about covered cases, not a substitute for reviewing the implementation and the requirements.
In this workflow, the developer remains responsible for deciding what “correct” means. TDD makes that decision more concrete and repeatable; it does not transfer responsibility to the test suite or to the assistant.
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Does “Nobody Did TDD for 25 Years” describe the industry?
No verified evidence in the available sources establishes that developers broadly avoided TDD for 25 years. The phrase is best read as the headline’s provocation, not as a measured adoption statistic. An older excerpt from Succeeding with Agile repeats historical claims about TDD studies, including reported changes in development time and bug counts, but those claims are secondhand here and their original studies were not checked. They should not be treated as established figures.
The narrower, supportable point is that machine-generated code makes executable specifications especially useful: a test can provide a concrete target and a repeatable way to check it. Whether that target is good enough depends on the tests’ coverage and quality.
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