🚀 Boost your IT career with REST API automation! 💪 Gain hands-on skills to ace your API testing interviews! 🔗 Integrate API automation seamlessly into CI pipelines! 🤖 Stay ahead with AI model testing techniques! 📚 Build expertise through real-world AI testing examples!
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.
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 Speech-to-Text API
Overview of 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 Text-to-Speech API
Overview of 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 Translation AI
Overview of 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.