Use AI to generate unit and E2E tests, expand coverage, and provide automated PR feedback — reducing manual test authoring and surfacing issues before human review.
Engineers define test strategy, validate reliability, confirm assertions reflect real requirements, and remain the final gate for quality and correctness.
AI generates unit tests (including edge cases and mocks) for selected functions/classes, which engineers refine to match real behavior.
Qodo (CodiumAI) / GitHub CopilotUse AI inside the editor to turn written user journeys into Playwright E2E tests that run in CI and are reviewed like normal code.
Playwright / GitHub CopilotAn AI review bot leaves inline suggestions on the PR for naming, readability, missing tests, and potential bugs so the author can fix issues early.
CodeRabbit / Cursor ReviewI’ve been using the Copilot CLI on a daily basis for coding, review, planning, design, and for debugging production systems. It’s awesome! Glad to see it reach GA. 👏
I've been using Claude Code daily for 6+ months. It's replaced most of my routine work:
• To understand code base - yes true this is game changing for me. its creates nice tech documents.
• Boilerplate generation
• Refactoring large codebases
• Debugging edge cases
• Writing tests & automation scripts - top notch in UT's
It outperforms Cursor and GitHub Copilot for deep reasoning and architectural changes. Period.
⚠️ BUT — always validate. Review the code. Run your tests. You're still the final gatekeeper. AI hallucinates less here, but "less" ≠ "never."
I work at a small B2B SaaS startup where leadership is getting "pro-AI" due to customer expectations. I'm not anti-AI (I use it daily), but our non-technical PM installed Cursor + Claude, built a quick demo app, and now wants to "prototype" features for engineering and even push vague initiatives like "use AI for automated E2E testing" despite us lacking solid testing strategy, acceptance criteria discipline, and clear system boundaries. I'm looking for advice on how to prevent reckless AI-driven development expectations from taking over before it becomes politically impossible to stop.
In my previous post I listed the top 5 things I use AI for apart from writing code.
Reena Garg asked which AI tools I use for these tasks 🙏 — sharing my list👇
⭐️ Thinking partner for ideas Tool: Cursor (Plan/Ask mode) I use Plan mode to discuss a feature/design: “what am I missing?”, “what can go wrong?”, “what’s the simplest version?”, “how should I break this into steps?”, “How can we implement this?”,
⭐️ Finding edge cases Tool: Claude (Opus-4.6) I paste the context (API, flow, PR description) and ask it to list edge cases: retries, timeouts, caching, permissions, weird inputs, etc. Opus works great for any code related tasks.
⭐️ Code review before the code review Tool: CodeRabbit, GitHub Copilot, Cursor Review These tools are really great to get a review before asking for a human review. I use them on all of my PRs.
⭐️ Turning manual processes into clean steps Tool: ChatGPT / Claude This is usually a back-and-forth conversation until it becomes a clean checklist/runbook and sometimes an automation script.
⭐️ Writing clearer communication Tool: Any AI works, but I mostly use ChatGPT PR descriptions, Slack updates, incident notes — it helps turn messy thoughts into clear writing.
If you’re using any AI tools in a different way, please share — I’d love to learn and try them too 🙌
Cheers, Princi 👩🏻💻