Artificial Intelligence (AI) and Machine Learning (ML) have been increasingly integrated into test automation to improve the efficiency and effectiveness of software testing. It's potential to greatly enhance testing processes by making them more efficient, accurate, and adaptive.

1. Testing in
AI(Artificial
Intelligence)
Presenter Name
Soniya Raichandani

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3. 1. Introduction to Artificial Intelligence
2. Why AI Testing?
3. Benefits of AI testing
4. Automation Testing Tools with AI
5. What is Visual Testing?
6. Challenges in AI-Driven Automation
Testing
7. AI-Driven Testing Approaches
8. Case Studies
9. Demo

5. Artificial Intelligence
 Artificial intelligence (AI) is the simulation of
human intelligence in machines that are
programmed to think and act like humans.
Learning, reasoning, problem-solving,
perception, and language comprehension are all
examples of cognitive abilities.
 Machine learning is a form of artificial
intelligence based on algorithms that are trained
on data.
• Strong AI
• Weak AI

6. Why AI Testing?
• AI is used to make testing more accessible and
more efficient. It helps automate the testing
process, such as creating test cases,
running tests, analyzing results, and
spotting errors.
• AI testing is the process of evaluating
the functionality, performance, and
reliability of a system with the help of AI.

7. AUTOMATION
DECISION MAKING
DATA ANALYSIS
PROBLEM SOLVING
INNOVATION

10. What is visual Testing?
• Visual testing is a software testing technique that evaluates the visual appearance
and behavior of a software application’s user interface (UI) or graphical user interface (GUI).
• Visual Testing checks that the software appears to users
as it should.
Some of these can be:
 Manual
 Pixel-by-pixel
 DOM-based
 AI-Driven

11. Examples of AI
 Smart Element Locators
 Test Data Generation
 Visual Validation
 Dynamic Element
Identification
 Self-healing Tests
 Performance testing with AI
 Automate bug Triage

14. Tasks can AI Software Testing
help
Tasks can AI Software Testing not
help
• Creation of test case
• Execution of test case according to
changes
• Test Planning
• Automation of the same type of
workflows
• Maintenance of test cases when there
are changes in the code
• UI testing
• Performance and Load testing
• Testing before releases
• Create Test Plans automatically
• Review of Documentation
• Test Creation for complex scenarios
• Test Result Reporting and analysis
• UX testing

15. Artificial Intelligence Test
Automation Tools

16. • You can use Applitools to test web apps,
mobile apps, desktop apps, PDFs,
screenshots, and more.​
• Applitools offers Eyes SDKs that support
several popular testing frameworks in multiple
languages, such as Selenium, Cypress,
and Playwright.
Applitools is a platform for AI-backed test
automation:
•With Visual AI and Eyes, you can perform visual
assertions that reduce automation
development efforts while improving coverage.​
•With the Ultrafast Grid, you can scale your tests
to run against any browser or mobile device at
lightning speed.​

17. AI Testing Approaches
1. Differential testing
•Compares different versions of an application
•Identifies differences and classifies them
2. Visual testing
•Focuses more on the visual and interactive
aspects of an application
•Uses image-based learning and screen
comparisons
3. Declarative testing
•Uses natural or domain-specific language to
specify test intent
•Tells the computer what to test
4. Self-healing automation
•Automatically corrects element selection in
tests
•Ensures tests are accurate

18. CASE STUDIES : AI
Healthcare Finance
Autonomou
s Vehicles
Retail
Customer
Service
Education
Manufactui
ng
Marketing
AI has been applied across various industries and domains.