𝗧𝗵𝗲 𝗔𝗜 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗧𝗿𝗮𝗽
You hear someone say "we shipped 40% more tests this quarter" and everyone nods.
I saw this happen at a SaaS company in Tokyo. The QA lead was proud. Management was happy. The pipeline was green.
Six weeks later, a payment system broke for 72 hours. No one noticed because the AI wrote tests that checked for "no errors" instead of "correct data."
This is Testing Blindness.
It happens when your team generates many tests but cannot tell when those tests lie to you. AI makes it easy to mistake test coverage for test quality.
A recent post on Qiita shows this exact struggle. An engineer used AI to handle projects with no automation. Tests came fast. Metrics looked great.
But the engineer had to learn Playwright and API testing manually. Why? Because the AI could write syntax, but it did not understand how the system worked.
Testing Blindness has three main symptoms:
• Assertion Atrophy: Tests pass because they check if the code crashes, not if it works correctly. • Boundary Case Blindness: AI focuses on "happy paths." It misses edge cases like null inputs or race conditions. • Regression Confidence Inflation: You feel safe because test counts doubled. In reality, you just doubled your false confidence.
In my experience, teams go from zero tests to 1,200 tests in months using AI. The reports look perfect. The actual bug detection rate drops.
In Japan, the focus on management and process (kanri) can make these high numbers feel like success. In the West, teams often skip tests because AI makes it easy. Both paths lead to production failures.
AI optimizes for metrics while hurting your ability to debug.
If you use AI in QA, follow these rules:
- Audit tests weekly: Pick 5 random AI tests. Ask: "What would make this test pass incorrectly?" If you cannot answer fast, you have a blind spot.
- Set a boundary quota: For every 10 AI tests, write 2 edge case tests manually.
- Use the 3am test: Ask if these tests would catch a failure at 3am. If you are not sure, they are not good enough.
- Keep one module manual: Test one critical section by hand. This keeps your debugging skills sharp.
Do not mistake test volume for test quality. Do not let efficiency replace judgment. The tests that save you are the ones you actually understand.
Has your team seen a drop in testing quality since using AI? Share your experience below.
جامعه یادگیری اختیاری: https://t.me/GyaanSetuAi