Vibe testing in 2026: Manual Testing with AI Tools

Explore two levels of ai tools for testers: level 1 conversational ai (chatgpt, claude, gemini) and level 2 gentik ai that act in your repo or live app.

Kersa, Windsorff, and GitHub Copilot demonstrate agentic AI tools that help testers understand code and business logic, generate test plans from repository artifacts, and act on pull requests and issues.

Antigravity connects to your repository and live browser to analyze the UI and generate code-base grounded test ideas for testers, not developers.

Explore level 1 conversational tools for testers, including Claude's structured outputs and persistent memory projects, ChatGPT's batch tasks, and Gemini's Google workspace integration.

Adopt a practical decision framework to match tools to tasks, using code-based copilots (GitHub Copilot) or chatbots (Claude, ChatGPT) and Antigravity for exploratory testing, while sharpening prompts and critical thinking.

Choose one AI tool, either Claude or chat GPT, and master it through repeated test cases. Avoid tool hopping; build speed and judgment before adding a second tool.

Set up nine free AI testing tools—ClaudeWeb, ClaudeDesktop, MCP, ChadGPT, Gemini, Gyra, GitHub Copilot, Cursor, wind surf as a GENTIG IDE option, and An Antigravity—to ensure frictionless demos.

Set up Claude, ChatGPT, and Gemini to perform vibe testing with AI tools; explore browser-based, no-install access, sign-up steps, and Google Workspace integration for generating content in Google Docs.

Install Claude Desktop with your existing Claude account to access MCP tools and connect to gyra, a capability not available in the web version.

Install gyra and UV, then set up a techshop testing project with key tech. Follow the mcp-setup-guide to generate an api token, configure, and verify the tech project.

Set up GitHub Copilot as the agentech tool for test case generation and AutoFix PR workflows by creating an account, enabling Copilot, and using the Copilot chat in VS Code.

Install and evaluate cursor and wind surf as AI native code editors; sign in, activate both, and explore cursor’s chat panel and wind surf’s cascade panel for test plan generation.

Set up antigravity and run your first vibe test, using Claude to identify key login form issues, including password field type, no empty-field validation, and missing error messages.

Run an AI-assisted test to surface defects in minutes, from passwords to low-contrast elements. Embrace the test loop—explore, ask, and document—as the foundation for section four is waiting.

Meet TechShop, a four-page demo e-commerce app built with plain html, css, and javascript, and practice bug hunting across BrokenApp with 18 intentional bugs.

Explore the app as a first-time user to spot defects like a broken image and a dead checkout button, then use ai-assisted developer tools to reveal an empty-order bug.

Use ai to build a feature inventory from source code by extracting index.html and app.js with browser dev tools, and outline features and validations for testing.

Master prompt engineering to tailor AI test outputs with concrete login context, avoiding vague prompts, using structured prompts that specify login rules like minimum password length and three failed attempts.

Learn to craft effective testing prompts using the four-part structure: role, context, task, and format, including concrete login form examples and output specifications.

Learn how iteration and follow-up prompts turn AI into a conversational partner, using four-part prompts to generate and refine UI test cases, edge cases, and exact error text.

Develop a testing instinct through pattern recognition and critical evaluation of real-world apps, while leveraging AI tools to accelerate identifying missing validations and the delta to good software.

Begin with a test plan that comes before test cases, defining scope, approach, entry criteria, exit criteria, prioritization, and risks, then write and run test cases inside that scope.

Master a chatbot-driven test plan using Gemini and Google Drive to convert sprint requirements into a structured plan, covering login, product catalog, shopping cart, and check out form.

The cascade agent in Windsurf reads repo requirements and bug lists to generate a grounded sprint test plan with entry, exit criteria, prioritisation, risks, and saves it to the project.

Senior testers plan before execution, using AI-assisted planning that reads requirements and bug lists to ground the test plan, and exit criteria that end arguments with measurable states.

Discover why Jira is the issue tracking tool used by the majority of software teams and how logging test cases, bugs, and results in Jira connects you with hiring managers.

Explore how MCP, Model Context Protocol, enables Claude to create Gyro tickets directly, reducing manual work and copy-paste, and verify the connection via Claude Desktop with a Gyro project list.

Learn how we structure test cases in Jira using gyrotasks integrated with MCP, including summary, pre-condition, steps, expected result, labels, and priority, enabling AI-driven end-to-end testing.

Define test scenarios and convert them into full test cases for an e-commerce app using Claude Web, OpenClaw.AI, and a Techshop project, focusing on login, catalog, cart, and checkout.

Apply the agentic testing method using Claude Desktop to read project files and generate positive test scenarios for log in, product catalog, shopping cart, and checkout.

Master the four-pass method to review AI-generated test cases, ensuring coverage, relevance, accuracy, and gaps while you own the test suite despite faster ticket creation.

Explore the full agentic loop that cuts the QA cycle from days to minutes by using GitHub Copilot to auto-write required-field validation and open pull requests.

The four-pass review—coverage, relevance, accuracy, gaps—defines your expertise in test case design. AI handles formatting and first drafts; you provide judgment, domain knowledge, and sign off via pull requests.

Learn autonomous exploratory testing with antigravity that designs and runs tests in real time, revealing live UI bugs in the TechShop e-commerce app.

Review Antigravity's findings to confirm bugs such as a wrong discount total, non-responsive checkout, plain-text password, and cart total not updating, reproduce, filter noise, and export notes for Claude.

Explore a seamless workflow from exploratory testing findings to professional bug reports using Antigravity, Claude, and Jira, transforming notes into Gyra tickets with environment and severity.

Launch an ai agent to explore a full app—from login to checkout—and surface bugs. Judge the output with professional judgement, filter noise, and log bugs in gyra in 15 minutes.

Craft precise bug reports by naming a specific, action-oriented title, documenting environment, severity vs priority, and step-by-step reproduction, then attach visuals to ensure quick fixes and professional credibility.

Generate professional bug reports with ChatGPT, batch-process findings from a shopping cart exploratory session, and prepare five reports for logging in gyra.

Discover how to perform vibe testing in 2026 using Cursor and MCP to generate grounded bug reports with exact code references and auto-create Jira tickets.

Learn the difference between severity and priority: technical impact versus business urgency, using the critical, high, medium, and low scale with practical bug examples.

Turn every bug report into a professional, structured document with clear titles, steps, and accurate severity, verified by ai; your findings land every time, building trust and collaboration with developers.

Testers compare bug fix verification with regression testing to ensure fixes hold in the Fixed TechShop App, using AI-assisted manual testing to speed and safeguard releases.

Create a verification checklist with Claude, fetch the known-bugs.md file, and generate three-step retest procedures for all 18 bugs, validating a complete verification cycle.

Learn a regression testing mindset using AI tools to pinpoint high-risk areas after a code fix, prioritizing cart totals, order summary, and promotional code logic for precise go/no-go decisions.

Master regression testing by running a complete manual testing cycle, writing test cases, building a test plan, and probing what else could this have touched to catch elusive bugs.

Learn to craft a test report that communicates what was tested, found, and remains open, with executive summary, scope, execution summary, defect summary, risk statement, and conclusion, powered by ai.

Count past, failed, and blocked cases; hand manual data to Claude; generate a professional test summary, refine conclusions, and set a conditional release path based on bug 009 and 014.

The agentic approach uses Claude Desktop to read Jira directly from the Gera Project Tech Sprint 1, counts tests and bugs, and generates a go-no-go report saved as test-report-sprint-1.

Map the complete manual testing cycle from understanding the app to release. Design test plans, cases, exploratory testing, bug reports, verification, and a go-no-go summary powered by ai.

Translate your testing report into actionable risk and coverage in a Go/No-Go recommendation, owning the conclusion and offering conditional ship criteria to build stakeholder trust.