Day 3: List ways in which AI is used in testing

AI in Software Testing: AI is increasingly integrated into software testing to automate tasks, improve accuracy, and optimize test processes. Common applications include test automation, visual testing, defect prediction, and performance testing.
Key AI Uses in Testing:

Self-Healing Tests: Tools like Katalon Studio and Functionize update test scripts based on code changes, reducing manual maintenance.

Test Case Generation: AI can create test cases from requirements or other inputs.

Visual Testing: Tools like Applitools and Percy use AI for UI consistency checks and visual validation.

Defect Prediction: AI identifies potential defects by analyzing code changes and historical data.

Performance Testing: AI simulates user behavior to optimize application performance

**Popular AI-Driven Testing Tools: **

Katalon Studio: Comprehensive automation across various platforms (web, mobile, etc.).

Applitools Eyes: Detects visual changes across different environments.

Testim: Offers self-healing and intelligent test case generation.

Functionize: Integrates AI for self-healing and predictive analytics.

BlazeMeter: AI for performance profiling and optimization.

DeepCode: AI-driven static code analysis tool.

Challenges with AI Tools: While AI can streamline processes and improve efficiency, it may not be suitable for all scenarios, especially those requiring deep contextual understanding or heuristic-based activities.

AI Limitations: Although AI can help with many aspects of software testing, human oversight is crucial for validating AI outputs and ensuring quality.