Explore the differences between AI-powered automated testing approaches and how each impacts test efficiency, reliability, and scalability. Session recording and more info at https://applitools.info/6i5. Applitools Technical Solutions Architect Lee Vardaro offers insights on: • When to use AI-assisted, AI-augmented, or Autonomous testing • How AI-powered testing improves coverage, reduces maintenance, and accelerates release cycles • Where AI delivers meaningful improvements—and where human oversight remains essential • Best practices for integrating AI into your existing testing strategy

1. AI-Assisted, AI-Augmented, &
Autonomous Testing
Choosing the Right Approach
Lee Vardaro Technical Solutions Architect @ Applitools

2. Agenda
✓ Defining the Landscape
✓ Core Components & Technologies
○ Coding Assistants
○ Test Case Generators
○ Test Code Generators
○ NLP Test Creation
○ Testing Agents
○ Visual AI for Testing
○ AI Analysis of Test Results
✓ Production Readiness
✓ Q&A

3. DEFINING THE
LANDSCAPE
AI-Augmented
Autonomous
AI-Assisted
✨ What are they?
✨ How do they differ from
traditional testing?
✨ What’s the role of human
oversights vs. full
automation?

4. DEFINING THE
LANDSCAPE ● AI engineering
● Prompt engineering
● Data security
● Cost of using AI API
In-House vs. 3rd Party AI
● Flaky tests
● Inconsistent results
Deterministic vs. Non-Deterministic
● The AI of today is not the AI of tomorrow
Rapid Change

5. CORE COMPONENTS & TECHNOLOGIES
● How they aid in
writing test-related
code
● Current maturity and
limitations in
production
Coding Assistants
● Automated creation of
test cases from logs,
user actions, and
design docs
● Real-world
effectiveness and
challenges
Test Case Generators
● Automation in
generating test scripts
● Balance between
speed and accuracy
● Test code not in actual
copy-paste format
Test Code Generators

6. CORE COMPONENTS & TECHNOLOGIES
● Converting plain language into
automated tests
● Defining individual steps vs.
intent driven testing
● Ability to record user actions
and convert to NLP
NLP Test Creation (LCNC)
● Autonomous/semi-autonomous
systems that mimic human testing
● Features like self-healing,
exploratory testing, and
AI-powered insights
● Production reality: benefits versus
areas needing improvement
Testing Agents

7. CORE COMPONENTS & TECHNOLOGIES
● Forecasting failures and
high-risk areas of applications
using data analytics
● Determining root causes of test
failure
● Gives an overall picture of the
health of both the application
and testing suite
AI Analysis of Application
Data & Test Results
● Types of tools for pixel-perfect
verification
● Current performance in
dynamic UI environments
● DOM snapshot vs. traditional
screenshots
Visual AI for Testing

8. Evaluating the Reality
PRODUCTION
READINESS
✨ Comparison to traditional
manual and automated testing
✨ Pros: Efficiency, scalability, and
cost-effectiveness
✨ Cons: Limitations in adaptability,
human intuition, and complex
scenarios
✨ Case insights: Where AI is
effectively deployed and where
it still falls short

9. Want to explore AI-powered testing
with Applitools?
Schedule a call with a
Testing Specialist
applitools.info/v2j
Questions??