Why do my AI-generated tests pass on the bugs they were meant to catch?
機会
AI coding assistants generate unit tests by reading the implementation, so assertions reflect what the code currently does rather than what it should do. A test written against a buggy function learns the bug as a fixture, not a violation. Developers shipping with 90% AI-generated coverage see mutation scores stay flat and real regressions slip through unchanged. The core issue is the oracle problem: there is no ground truth for correct behavior unless you supply it, and AI assistants have no access to the requirements or intent behind the code. Research from July 2025 confirms that LLM-generated tests frequently pass on the exact buggy code they were written against, failing only when the bug is fixed.
重要な理由
A test suite that passes on broken code is worse than no test suite, because it turns the primary quality signal into a false guarantee.
機会をどう評価するか
Opportunity Scoreは測定値ではなく、私自身の見解です。どれほど痛みを伴うか、どれほど頻繁に影響を与えるか、そして今日時点で解決策がいかに少ないか。スコアが高いほど、構築する価値が高いと私は考えています。
それが現れたときにどれほどの痛みをもたらすか。
実際にどれほど頻繁に人々がそれに直面するか。
今日時点で、それに対する優れたツールがいかに少ないか。