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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchPassing tests show that software behaves as expected for the cases in its fixtures. They do not show that those cases resemble the world the software is meant to handle. In a 2026 essay, software author Remus Lazar describes how test data that looked tidy helped a charging-station deduplication bug survive a refactor—and how checking real records exposed the gap.
How a passing test suite missed duplicate charging stations
Lazar says a job that combined charging-station listings from sources including a German federal register, roaming networks and Tesla had run for fourteen months. After a May refactor, the new test suite passed. Later, two map pins appeared where there should have been one charging site.
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In the test fixtures, records for the same site shared identical operator names. Lazar says production duplicates often had operator labels from different organizations, so the strings did not match. The tests had verified the behavior of the examples they were given, but those examples encoded an unrealistic clue about what made two records duplicates.
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What tests can prove—and what they cannot
A test suite is evidence about the cases it exercises. If a test passes, it establishes that the implementation produced the expected result for that fixture under that test. It cannot establish that the fixture captures important real-world variation, or that the expected result is the one users need.
| Check | What it can establish | What it may miss |
|---|---|---|
| Fixture-only test | Whether code handles the constructed examples as expected. | Whether the examples reflect production data or test the right assumption. |
| Real-data check | How the implementation behaves on records drawn from the actual domain. | Whether a technically plausible result improves the user’s experience. |
| User-visible outcome measure | Whether the result users encounter—such as duplicate map pins—changes in the intended direction. | It does not by itself explain which implementation detail caused the outcome. |
The strongest lesson is not to replace unit tests with production checks. The checks answer different questions: fixtures help isolate behavior, real examples challenge assumptions about the data, and outcome measures show whether the product got better in a way users can see.
Why the assumption mattered more than the diff
The faulty clue was operator-name equality: two records with the same operator name seemed to stand for the same place. In the reported production data, organizations behind listings did not reliably share a name even when the listings described one site. A test could therefore be clean, deterministic and fully green while rehearsing the wrong model of reality.
Lazar’s account is not evidence that AI agents uniquely create this kind of mistake. Its more specific warning is that agent assistance can speed up implementation while leaving assumptions unchallenged—especially if the developer is no longer spending as much time examining real examples and deciding what the system should mean.
How to review code and the world it represents
Lazar recommends two kinds of review. The first examines implementation: read the code rather than relying on an agent’s summary, inspect edge cases, keep changes small enough to understand, remove code you cannot justify, and ask whether each test would fail if the intended behavior broke.
The second examines the concept being implemented. For software that models the outside world, his suggestions include:
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- Include at least one fixture taken from real data, with appropriate care for privacy and sensitive information.
- Measure a user-visible result, not only an internal algorithm activity such as how many pairs were compared.
- Pay attention to comments that signal friction or uncertainty in the design.
- Inspect the product itself to see what users actually encounter.
These are Lazar’s recommendations from a specific engineering case, not a guarantee that one real-data fixture or metric will catch every defect. Their value is that they make a team ask whether its tests validate the user’s problem or merely the code’s current assumptions.
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Lazar says the fix was also written with an agent. He changed the prompt from preserving prior behavior to measuring the user-visible result against a production snapshot. The replacement matched records using distance and street name and avoided depending on the order in which records were processed. He says the work took four days and that two further corrections emerged during dry runs against real data.
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He also reports that the refactor was merged 78 minutes after it was opened, without review, and takes responsibility for that failure. Separately, he says the median change in his repositories that summer was around 35 added lines while the number of changes more than doubled. Those details frame a practical trade-off: small diffs are easier to inspect, but speed and a compact patch do not substitute for reviewing the assumptions, data and product outcome.
The question to ask before trusting a green build
Ask: “Where did the test data come from?” If the software makes claims about messy external reality, follow that with two more questions: does at least one test reflect a real case, and is there a measure of what the user sees? Lazar’s essay puts the point plainly: “Test data that nobody took from reality does not test the concept.”
Source: Remus Lazar, “The Test Data I Did Not Write” (2026). The incident, counts and timeline above are the author’s account.
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