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Duration 21 hours
Course Outline
Getting Started with AI in QA
- Defining Artificial Intelligence
- Comparing Machine Learning, Deep Learning, and Rule-based Systems
- How AI is evolving software testing
- Primary advantages and obstacles of AI in QA
Foundations of Data and ML for Testers
- Distinguishing between structured and unstructured data
- Understanding features, labels, and training datasets
- Exploring supervised versus unsupervised learning
- Basics of model assessment (accuracy, precision, recall, and more)
- Examining real-world QA datasets
AI Applications in Quality Assurance
- Generating test cases with AI assistance
- Predicting defects through ML algorithms
- Optimizing test prioritization and risk-based strategies
- Implementing visual testing via computer vision
- Analyzing logs and identifying anomalies
- Applying Natural Language Processing (NLP) to test scripts
AI Toolkits for QA
- Surveying AI-enabled QA platforms
- Leveraging open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras) for QA prototypes
- Understanding the role of LLMs in test automation
- Developing a basic AI model to forecast test failures
Embedding AI into QA Processes
- Assessing the AI-readiness of your current QA operations
- Merging AI with continuous integration: integrating intelligence into CI/CD pipelines
- Creating intelligent test suites
- Handling AI model drift and managing retraining schedules
- Ethical implications of AI-driven testing
Practical Labs and Capstone Project
- Lab 1: Automating test case generation with AI
- Lab 2: Constructing a defect prediction model from historical test data
- Lab 3: Utilizing an LLM to analyze and refine test scripts
- Capstone: Full implementation of an AI-powered testing pipeline
Requirements
Enrolled participants are anticipated to possess:
- A minimum of two years of professional experience in software testing/QA positions
- Proficiency with test automation frameworks (such as Selenium, JUnit, or Cypress)
- Fundamental programming skills (ideally in Python or JavaScript)
- Working knowledge of version control and CI/CD platforms (like Git and Jenkins)
- No previous AI/ML background is necessary, but a strong curiosity and readiness to explore are strongly encouraged
Testimonials (4)
The possibilities of postman and future use of it.
Gordana Gacic - SEE Digital D.O.O.
Course - API Testing with Postman
hands on exercises, easier to retain information
ashley bolen - Insurance Corporation of British Columbia
Course - Test Automation with Selenium
The exercises we covered in the course were quite useful and applicable to my activities at work. The doubts were resolved, and the examples shared are very helpful.
jocelin salas - BANXICO
Course - Test Automation with Selenium and Python
Machine Translated
The way technical topics were addressed in a practical manner, with real examples and an excellent attitude from the instructor.
Juan - ASECCSS
Course - Automatización de Pruebas con Selenium
Machine Translated
