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Duration 21 hours
Course Outline
Introduction to AI for QA
- Defining Artificial Intelligence
- Distinguishing between Machine Learning, Deep Learning, and Rule-based Systems
- The trajectory of software testing in the age of AI
- Major benefits and hurdles of AI adoption in QA
Data and ML Basics for Testers
- Understanding the difference between structured and unstructured data
- Concepts of features, labels, and training datasets
- Overview of supervised and unsupervised learning
- Introduction to model evaluation metrics (accuracy, precision, recall, etc.)
- Application of real-world QA datasets
AI Use Cases in QA
- Automating test case creation with AI
- Predicting defects using ML algorithms
- Strategies for test prioritization and risk-based testing
- Leveraging computer vision for visual testing
- Analyzing logs and detecting anomalies
- Applying Natural Language Processing (NLP) to test scripts
AI Tools for QA
- Survey of leading AI-enabled QA platforms
- Utilizing open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras) for QA prototyping
- The role of LLMs in 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
- Embedding intelligence into CI/CD pipelines via continuous integration
- Designing intelligent and adaptive test suites
- Oversight of AI model drift and retraining cycles
- Ethical implications of AI-powered testing
Hands-on Labs and Capstone Project
- Lab 1: Automating test case generation with AI
- Lab 2: Building a defect prediction model from historical test data
- Lab 3: Leveraging an LLM to review and refine test scripts
- Capstone: Implementing an end-to-end AI-powered testing pipeline
Requirements
Participants should bring the following to the table:
- At least two years of experience in software testing or QA roles
- Proficiency with test automation tools (such as Selenium, JUnit, or Cypress)
- Foundational programming knowledge, preferably in Python or JavaScript
- Hands-on experience with version control and CI/CD tools (e.g., Git, Jenkins)
- No prior AI/ML background is necessary, but a strong curiosity and openness to experimentation are key
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 4800 € + VAT*
Contact us for an exact quote and to hear our latest promotions
Testimonials (3)
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
Key topics can be discussed and agreed upon with the trainer in advance. Relaxed and pleasant atmosphere during the seminar days.
