🤖 Generating Test Cases from User Behaviour: Is AI Ready?

By Divya Singla, QA Architect | Automation Strategist

🧩 Introduction

As AI continues to reshape Quality Engineering, one trend gaining serious traction is the use of AI to generate test cases directly from real user behavior.

Traditionally, we write tests based on specs or requirements. But what if AI could watch how users interact with our product and then create test cases from those journeys?

This shift — from spec-driven to usage-driven testing — promises higher relevance, faster feedback loops, and better risk coverage. But is AI ready to take on this challenge?

🧠 What Does It Mean?

Generating test cases from user behavior means using AI to:

• Track real user journeys through your application
• Detect frequent paths, edge cases, and anomalies
• Auto-generate test scripts based on what actually happens

Instead of writing hundreds of test cases from scratch, the AI proposes flows that mirror what users are actually doing — or struggling with.

🔍 Why QA Teams Are Exploring This

Top reasons this approach is gaining popularity:

• 🎯 Better test coverage — AI surfaces real scenarios, not just hypothetical ones.
• ⚡ Accelerated regression checks — Your test suite aligns with usage.
• 🔄 Dynamic adaptation — As user behavior evolves, so does your test suite.
• 🔥 Anomaly detection — Edge cases and unexpected flows are highlighted early.

🧪 How It Works (Simplified Workflow)

1. Capture user behavior — Use tools like Segment, PostHog, or Mixpanel to collect real-time event data from users.
2. Clean and analyze — Use clustering or pattern mining to group journeys.
3. Apply ML models — Detect patterns using models like LSTM or transformers.
4. Generate test logic — Feed journeys into templates or use an LLM like GPT to create test code.
5. QA validation — Human-in-the-loop validation ensures accuracy and context.

🧰 What Tools Are Out There?

Many platforms are experimenting with this space. Here are some you may encounter:

• Testim / Tricentis — Suggest test scenarios based on observed behavior.
• Launchable — Uses machine learning to suggest which tests to prioritize based on recent changes.
• PostHog + custom ML — Teams are building pipelines that use open-source analytics + GPT to write Cypress tests.
• Selenium + GPT-4 — Some advanced orgs use LLMs to generate test scripts based on traffic logs.

🛠️ Sample Example: AI-Generated Test Case (Cypress)

Let’s say your analytics show that 800 users followed this path:

A → B → D (success)

While 1,200 followed:

A → B → C (error encountered)

Your AI tool identifies these and suggests a test to reproduce the failing path.

// Auto-generated Cypress test based on error path
describe('Checkout Flow - Error Path', () => {
  it('should navigate from A to C via B and validate error', () => {
    cy.visit('/pageA');
    cy.get('button#toB').click();
    cy.get('button#toC').click();
    cy.get('.error-message').should('be.visible');
  });
});

You didn’t write this. The AI did — based on what users experienced.

🚨 What AI Can and Can’t Do (Yet)

✅ AI is ready to help with:

• Enhancing your regression suite with usage data
• Prioritizing tests based on high-traffic paths
• Surfacing edge cases not documented in specs

❌ AI is NOT ready to:

• Replace testers or test architects
• Understand nuanced business rules
• Predict test data complexity
• Validate UI/UX or visual quality

⚠️ Risks to Watch Out For

Common concerns when using AI to generate tests:

• ❓ Duplicate or meaningless tests — AI might misunderstand intent.
• 🔁 Test bloat — Too many low-impact scenarios clog your suite.
• 📉 Over-prioritization of popular paths — Rare but critical paths might get missed.
• 🔐 Privacy concerns — User data could be misused or exposed.

🧭 What QA Leaders Should Do

1. Start with observability — Work with your product and analytics teams to ensure you’re collecting clean, structured user journey data.

2. Add AI incrementally — Don’t expect an end-to-end pipeline right away. Start by analyzing journeys and generating suggestions, not replacing everything.

3. Keep humans in the loop — Human QA engineers are still vital to apply business knowledge, context, and critical thinking.

4. Monitor ROI closely — Measure whether these AI-generated tests actually catch bugs, reduce regression escapes, or speed up delivery.

🎓 Final Thoughts

The dream of AI autonomously writing useful, relevant tests is starting to take shape — but we’re not at full maturity yet.

That said, usage-based test generation is no longer theoretical. It’s a practical, incremental capability that can enhance your existing QA strategy.

As a QA leader, now is the time to:

• Pilot this approach with side projects
• Upskill your team in observability + AI fundamentals
• Rethink test strategy through the lens of real usage

💬 What’s Your Take?

Are you using user journeys to drive your tests? Would you trust AI-generated tests in production pipelines?

Let’s connect and discuss!