Скачать или смотреть 🚀 From Clicks to Code: The AI Prompt That Writes Your Tests – From Basics to Advanced

1. What This Is

This is your ready-to-use prompt workbook for transforming click-through test ideas into fully coded test cases using AI. Whether you’re a QA engineer moving from manual tools (clicks) or an automation engineer who wants to generate code-first tests, this covers from basic prompts to advanced prompt-engineering commands. It helps you move from “click here” flows to automation scripts with minimal manual scripting.

2. Why QA Engineers & Automation Engineers Should Care

Capture clicks to code: Automatically convert manual click flows into structured test cases and automation code.

Faster coverage, higher scale: Use AI prompts to generate both UI scripts and API tests, with edge/negative cases included.

Reduce manual scripting: Instead of writing every selector and step, craft a prompt and let AI scaffold code for you.

Maintainable automation: With advanced prompts you can specify patterns (Page Object Model, modular functions) so generated code is clean.

Prompt engineering mastery: As you move from basic descriptions to full automation commands, you build a reusable prompt library.

3. How to Use It (Step-by-Step)

Identify your click-flow: e.g., “User logs in, adds item to cart, checks out.”

Choose your prompt level (Basic → Intermediate → Advanced).

Use an AI tool and input your prompt with the relevant context (app type, tech stack, desired output format).

Run the prompt to get test cases (manual) or automation code (Cypress, Playwright, API).

Review the output: validate selectors, logic, assertions, modular structure.

Refine prompt if needed (add constraints, edge-cases, patterns).

Integrate generated code into your automation suite, version control it, maintain it like any other code.

4. Basic → Intermediate → Advanced Prompts

🔹 BASIC PROMPT

Click-flow: [MODULE / FEATURE DESCRIPTION]
Platform: [Web / Mobile / API]
Generate: 5 manual test cases converting click steps into structured format (preconditions, steps, expected result).


🔹 INTERMEDIATE PROMPT

Click-flow: [MODULE / FEATURE DESCRIPTION]
Platform: [Web / Mobile / API]
Generate: 10-15 test cases including functional, negative and edge-case flows.
Then: generate corresponding automation code snippets in [Tech Stack e.g., Cypress] for 3 of these.


🔹 ADVANCED PROMPT (Prompt Engineering Command)

You are a QA Automation Engineer.
Feature: [MODULE / FEATURE DESCRIPTION] on [TECH STACK e.g., Web React + REST API + Playwright].
1. Outline the test plan (scope, objectives, risk areas).
2. For the click-through flows, map each click step into a test scenario with unique ID, title, steps, expected results.
3. Then generate automation code: use Page Object Model (create separate classes/files), use descriptive selectors/data-cy attributes, include data-driven loops, assertions for positive/negative/edge cases.
4. Include comments in code and a doc-block at top explaining how to run and integrate into CI.
5. Lastly, generate a matrix of known flaky test signals (timeouts, race conditions, UI animation delays) and recommend how to mitigate them (retries, waits, isolation).

5. Five Fresh Prompt Variations You Can Use Today

“Translate this click-flow for our e-commerce checkout (select product → add to cart → apply coupon → remove item → checkout) into 12 test cases and automation code in Playwright.”

“Generate automation tests for our mobile onboarding flow (Android/iOS) including invalid permissions, network drop, resume after interruption.”

“Create a test plan and automation suite for our payments microservice UI: login → select plan → payment success/failure → receipt display; code in Cypress with POM.”

“Given the admin dashboard click-flow (login → navigate users → edit permissions → save → logout), generate manual test cases + UI automation code + edge-cases (invalid roles, expired sessions).”

“Rewrite the following manual click script into reusable automation code with separate POM classes, and annotate where test-data needs to be parameterised: [paste script].”

6. Things to Keep in Mind (2025)

Clear prompts matter: The better you describe the click-flow, tech stack, format, the better the output will be.

Prompt generation is not perfect: Generated code still needs review — selectors might be generic, assumptions may be wrong.

Use modular automation patterns: For large suites, specify POM, decouple data, avoid inline selectors. Otherwise tests may be brittle.

Treat prompt-engineering as code: Version your prompts, test them, refine them. You can have regressions if you change the prompt and output changes unpredictably.

It’s ideal for prototyping or speeding automation build-up; you still need full infrastructure (CI, real devices, test-data pipelines) for production-grade suites.

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