SKILL

ai-regression-testing

面向 AI 辅助开发的回归测试策略。包括不依赖数据库的沙盒模式 API 测试、自动化缺陷检查工作流,以及识别同一模型编写并审查代码时产生的 AI 盲点的方法。适用于为 AI 辅助代码补充回归覆盖,或同一模型既编写又审查某项改动时。

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Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

适用对象:软件开发与测试。自动收录 · 待核查。

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原文涵盖的主题

  • AI Regression Testing
  • When to Activate
  • The Core Problem
  • Sandbox-Mode API Testing
  • Setup (Vitest + Next.js App Router)
  • Test Helper for Next.js API Routes
  • Writing Regression Tests
  • Testing Sandbox/Production Parity

Testing patterns specifically designed for AI-assisted development, where the same model writes code and reviews it — creating systematic blind spots that only automated tests can catch. AI agent (Claude Code, Cursor, Codex) has modified API routes or backend logic A bug was found and fixed — need to prevent re-introduction Project has a sandbox/mock mode that can be leveraged for DB-free testing Running /bug-check or similar review commands after code changes Multiple code paths exist (sandbox vs production, feature flags, etc.) When an AI writes code and then reviews its own work, it carries the same assumptions into both steps. This creates a predictable failure pattern: Real-world example (observed in production): The pattern: sandbox/production path inconsistency is the #1 AI-introduced regression. Most projects with AI-friendly architecture have a sandbox/mock mode. This is the key to fast, DB-free API testing. The key principle: write tests for bugs that were found, not for code that works. The most common AI regression: fixing production path but forgetting sandbox path (or vice versa). Frequency: Most common (observed in 3 out of 4 regressions) Test to catch it: Frequency:

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原作者:affaan-m/ECC · 原作者仓库元数据。文档核查:未核查。最近来源检查:2026-11-09T15:01:43.366Z。

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