Masterclass

Testing AI-Generated Code: The New QA Playbook

Your dev team ships faster with Claude Code and Copilot. Learn how to catch what AI-generated code hides — before it hides from your users too.
Calendar 29th Sep, 2026
Clock 7 AM PT / 7.30 PM IST
Event Starts in
00
Days
:
00
Hours
:
00
Mins
:
00
Secs

Limited free seats. Book today!

By registering, you agree to Privacy Policy

Meet the Speakers

K V S Dileep
Head of Gen AI Education, Outskill
AI Researcher from IIT Madras, now training 50,000+ professionals in Generative AI concepts at leading education startups
Debasmriti Ghosh
Sr. Lead SDET, BrowserStack
11 years of experience in QA domain - designing, building, and maintaining robust automation frameworks across web, mobile and API platforms
Vinod Reddy
Sr. SDET, BrowserStack
AI testing pioneer specializing in Selenium, Playwright & WebdriverIO. Building innovative AI agents & LLM assertion frameworks
Gary Behan
Field CTO, BrowserStack
Advisor to senior leaders on automation strategy and driving impactful adoption of BrowserStack

From AI Code Review Blind Spots to a Complete AI-Code Testing Playbook

See a Real AI-Generated Feature Built Live
Watch a real feature get built live with Claude Code — the exact kind of AI-authored code your team is shipping today, and the starting point for everything you'll test in this session.
Build a Prompt-Driven Review & Authoring Harness
Turn that AI-generated diff into manual test cases, then automated tests — live, using Claude and Playwright — to surface untested edge cases and hallucinated logic before they reach CI.
Operationalize It With Skills/Agents
See how Test Companion, Test Management, and Skills/Agents take this from a one-off demo to a repeatable team workflow — plus real-device validation with Automate and App Automate.
Catch What Code Review Can't See
Root-cause a CI failure back to the actual AI-authored logic change, then catch the visual and accessibility regressions that "compiles fine" review always misses — using Percy and Accessibility Testing.
What You Will Achieve in This Masterclass
  • Catch hallucinated logic & untested edge cases
  • Scale test authoring with Skills/Agents
  • Validate AI code across real devices
  • Trace CI failures to the AI-authored change
  • Catch silent visual & accessibility bugs
  • Get a playbook for your team's QA strategy

Your Journey Through the New QA Playbook

Module 1

10 mins

The New Risk Landscape

Why AI-generated code breaks old QA assumptions, with real-world examples of what changes when a growing share of your codebase is AI-authored.

Module 2

5 mins

Generating a Feature Live with AI Coding Assistants

A real feature built live with Claude Code — the artifact every later module will test.

Module 3

40 mins

From Review to Rollout: Building the AI-Code Testing Harness

A continuous live build: from manual test cases to automated authoring, operationalized with Test Companion, Test Management, and scaled with Skills/Agents.

Module 4

10 mins

Validating Across Real Devices at Scale

Run the same AI-generated feature across a real device and browser matrix — the real-world conditions no AI coding assistant accounts for.

Module 5

20 mins

Automated RCA When AI Code Fails in CI

Feed a failing test into BrowserStack's RCA agent and trace the failure back to the actual AI-authored logic change — not a flaky test.

Module 6

5 mins

Catching Silent Visual & Accessibility Regressions

Run the same feature through visual regression (Percy) and accessibility scanning (Accessibility Testing) back to back — the bugs that "it compiles, ship it" reviews always miss.

Module 7

10 mins

Your Team's AI-Code Testing Playbook + Q&A

Leave with a synthesis checklist for what changes in your test strategy, review gates, and CI checks — plus an open floor for questions.

From AI-Code Blind Spots to a Complete Testing Playbook

See what changes when you have a real strategy for testing AI-generated code.

  • Highlights
Before (No ❌)
After (Yes ✅)
Before vs After
Code Review
no
Reviewing AI-generated code the same way you'd review human-written code, missing hallucinated logic and untested edge cases
yes
A prompt-driven review harness that surfaces what AI-generated code actually needs scrutinized
Test Authoring
no
Manually writing test cases to cover code you didn't design
yes
AI-assisted test authoring that scales from one PR to your whole pipeline with Skills/Agents
Device Coverage
no
Assuming AI-generated code "just works" across browsers and devices
yes
Real-device validation catching what the coding assistant never accounted for
Failure Analysis
no
Hours spent tracing a CI failure back to an opaque AI suggestion
yes
Automated RCA that traces failures to the actual AI-authored logic change
Visual & Accessibility
no
Silent visual regressions and accessibility gaps shipping in "working" AI-generated UI
yes
Visual and accessibility regressions caught before they reach production
Typical Result
no
Shipping AI-authored risk you can't see until it's in production
yes
A repeatable, team-wide playbook for testing AI-generated code with confidence

What Our Customers Have to Say

With AI-powered Visual Testing embedded in our quality pipeline, we’ve moved beyond code validation—into protecting the brand experience at scale, with precision and speed.
Zohar Liran
Director, Head of Engineering, Construction Platform, Autodesk
A test could come out true, but the screen does not look anything like you want it to look. That’s where visual testing with Percy comes in.
Ayushi Arora
SDET Lead, Nykaa
We have successfully brought down the regression time from 4 days to 1.5 days, resulting in short turnaround time on the life cycle of apps.
Vijay Selvam
Manager, Test Engineering, Swiggy
We will continue encouraging more teams to integrate automated tests with BrowserStack to increase their test coverage, reduce their feedback loops and deliver high-quality products faster.
Georgiana-Lucia Baragan
Senior Engineering Manager, Booking.com
The most major impact I have noticed is that we are able to deliver fast. We are also able to maintain the robustness and stability of our tests; the tests are more reliable now. And if anything fails, we have the video recording to go back and analyse where, when and why the test failed – is it a genuine issue or flakiness associated with our test case.
Harit Narke
Lead Software Engineer, Mastercard

Get Ahead of the Next Shift in QA

Your dev team is already shipping AI-generated code. Learn how to test it — live, hands-on, in one session.

Save My FREE Spot Now