SDET: Java REST API Automation with RestAssured and AI Models Testing

FEATURED

In today’s fast-paced tech industry, the ability to efficiently test and automate APIs is crucial for ensuring high-quality software. The “SDET: Java REST API Automation with RestAssured and AI Models Testing” course is designed to equip you with the skills needed to master API testing and leverage artificial intelligence to enhance your testing processes. Whether you’re a seasoned tester or new to the field, this course offers comprehensive coverage of essential topics, focusing on hands-on learning and practical applications.

Course Curriculum

Session 1: Introduction to API Testing

• Overview of API testing and its importance.
• Understanding REST architectures.
• Setting up the development environment.
• API Testing using Postman.

Session 2: Introduction to Rest Assured and Setting Up the Environment

• Understanding REST APIs and their significance
• Introduction to Rest Assured
• Setting up the development environment
• Installing Java, Maven/Gradle, and Rest Assured
• Writing and running your first Rest Assured test

Session 3: Writing and Executing Basic Rest Assured Tests

• Performing HTTP methods with Rest Assured
• Validating HTTP response status codes
• Extracting data from responses (JSONPath, XMLPath)
• Writing tests for different API endpoints
• Handling different response formats (JSON, XML)
• Practical exercises and hands-on practice

Session 4: Advanced Rest Assured Features and Best Practices

• Authentication and authorization (Basic Auth, OAuth)
• Using request and response specifications
• Parameterizing tests and data-driven testing
• Best practices for structuring and organizing tests
• Integrating Rest Assured with build tools (Maven/Gradle)
• Continuous integration and running tests in CI pipelines

Session 5: AI Models and REST APIs

• Overview of AI and machine learning
• Types of AI models: rule-based systems, machine learning models, deep learning models
• Pretrained models: advantages and use cases
• Supervised vs. unsupervised learning
• Key concepts: training, validation, and testing datasets
• Understanding the role of REST APIs in AI services

Session 6: Setting Up the Environment and Test Project

• Setting up the environment
• Writing and running tests
• Overview of AI model evaluation metrics (accuracy, precision, recall, F1 score)
• Importance of data quality and preprocessing in AI
• Hands-on practice with initial API tests

Session 7: Testing Google’s Speech-to-Text API

• Overview of Google’s Speech-to-Text API
• Sending audio data and receiving transcriptions
• Validating transcriptions against expected results
• Testing with different audio formats and languages
• Handling and testing noisy audio data
• Practical exercises and hands-on practice

Session 8: Testing Google’s Text-to-Speech API

• Overview of Google’s Text-to-Speech API
• Sending text data and receiving audio
• Validating generated speech against expectations
• Testing with different voices, speeds, and pitches
• Handling various languages and accents
• Practical exercises and hands-on practice

Session 9: Testing Google’s Translation AI

• Overview of Google’s Translation AI
• Sending text for translation and receiving translations
• Validating translations against expected results
• Testing with different language pairs
• Handling context-specific translations
• Practical exercises and hands-on practice

Session 10: Vision and Image Recognition Testing

• Overview of Google’s Vision AI
• Sending image data and receiving analysis
• Validating object detection, text recognition, and image labeling
• Testing with different image formats and scenarios
• Best practices for testing AI models via REST APIs
• Practical exercises and hands-on practice