从零到一搭建 AI 测试工作流：从需求到可执行测试
用一个可运行的 Python 工程，逐步建立需求识别、规则审阅、测试剧本生成、骨架填充和结果验证流程，附完整 Prompt、Skill 和公司接入方法。
#test-engineering
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AI testing workflow: from API material to acceptance
Explain how requirements, domain rules, scenarios, and existing SDKs connect in testing delivery, including where failures should return for correction.
Payments testing SDK: sharing requests without erasing business differences
Use multi-business reuse and existing subclass compatibility to explain request layering, calling contracts, and the limits of return-code retries.
XAT concurrency: method isolation, fixture scope, and deadline boundaries
Compare method and file subprocesses through shared fixtures, then follow discovery isolation, checkpoints, and parent-driven termination.
Who cleans up after test preparation fails? unittest and XAT
Follow partial setup, cleanup exceptions, and worker termination to understand cleanup registration, retained errors, and resource lifetime.
XAT reports: separating failure, missing results, and exit codes
Follow assertion failure, cleanup errors, and a worker crash through multiple issues, completeness, task identity checks, and command exit codes.
XAT: making concurrent test results trustworthy
Follow worker crashes and missing results through discovery, process scheduling, fixtures, method deadlines, and report validation.