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Duration 21 hours
Course Outline
Introduction to AI for QA
- The nature of Artificial Intelligence
- Distinguishing between Machine Learning, Deep Learning, and Rule-based Systems
- The progression of software testing through the adoption of AI
- Primary advantages and obstacles of integrating AI into QA
Data and ML Basics for Testers
- Differentiating between structured and unstructured data
- Exploring features, labels, and training datasets
- Concepts of Supervised and Unsupervised learning
- Basics of model assessment (including accuracy, precision, and recall)
- Application of real-world QA datasets
AI Use Cases in QA
- Generating test cases with AI assistance
- Predicting defects using Machine Learning
- Prioritizing tests and implementing risk-based strategies
- Conducting visual testing via computer vision
- Analyzing logs and identifying anomalies
- Utilizing Natural Language Processing (NLP) for test scripting
AI Tools for QA
- Survey of AI-enabled QA platforms
- Creating QA prototypes with open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras)
- Integrating LLMs into test automation
- Developing a basic AI model to forecast test failures
Integrating AI into QA Workflows
- Assessing the AI-readiness of your current QA processes
- Continuous integration and AI: embedding intelligence into CI/CD pipelines
- Architecting intelligent test suites
- Monitoring AI model drift and managing retraining schedules
- Ethical implications of AI-driven testing
Hands-on Labs and Capstone Project
- Lab 1: Automating test case generation with AI
- Lab 2: Creating a defect prediction model from historical test data
- Lab 3: Leveraging an LLM to review and refine test scripts
- Capstone: Full-scale implementation of an AI-driven testing pipeline
Requirements
Candidates should possess the following:
- At least two years of experience in software testing or QA positions
- Proficiency with test automation frameworks (e.g., Selenium, JUnit, Cypress)
- Fundamental programming skills, ideally in Python or JavaScript
- Hands-on experience with version control and CI/CD systems (e.g., Git, Jenkins)
- No prior background in AI/ML is necessary, though an inquisitive mindset and a desire to experiment are crucial
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
