Why can I tell a test is flaky but never find out why?
机会
Detecting that a test is flaky is mostly solved; several tools do it reliably across reruns. Diagnosing the specific root cause is not. LLMs evaluated on real-world flaky test datasets achieve F1 scores above 0.88 for detection but drop below 0.57 on the same inputs when asked to identify root cause. The few automated repair tools only handle order-dependent or implementation-dependent flakiness and fail on resource contention, async timing, and environment drift. Developers spend hours reading logs and adding print statements, then watch the failure refuse to reproduce locally.
为什么重要
Root cause attribution is the missing step that turns a flakiness detector from a dashboard metric into a tool that actually reduces CI time.
我如何评估机会
机会评分是我的个人判断，而非量化指标：痛苦程度、发生频率，以及当前解决方案的匮乏程度。分数越高，意味着我认为越值得去构建。
出现时造成的痛苦程度。
人们实际遇到它的频率。
当前针对它的优质工具有多匮乏。