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Duration 21 hours
Course Outline
Foundations of AI in QA
- The basics of Artificial Intelligence
- Distinguishing between Machine Learning, Deep Learning, and Rule-based Systems
- The progression of software testing driven by AI
- Primary advantages and potential challenges of AI in QA
Essential Data and ML Concepts for Testers
- Differentiating between structured and unstructured data
- Understanding features, labels, and training datasets
- Overview of Supervised and Unsupervised Learning
- Introduction to model assessment metrics (accuracy, precision, recall, etc.)
- Analysis of real-world QA datasets
Practical AI Applications in QA
- Automating test case generation with AI
- Predicting defects using ML techniques
- Optimizing test prioritization and risk-based strategies
- Implementing visual testing via computer vision
- Performing log analysis and anomaly detection
- Applying NLP for automated test scripts
AI Tools and Technologies for QA
- Survey of AI-enabled QA platforms
- Leveraging open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras) for QA prototypes
- Role of LLMs in test automation
- Creating a basic AI model to forecast test failures
Embedding AI into QA Workflows
- Assessing the AI-readiness of current QA processes
- Integrating AI into CI/CD pipelines for continuous intelligence
- Architecting intelligent test suites
- Handling AI model drift and managing retraining schedules
- Ethical implications of AI-driven testing
Practical Labs and Capstone Exercise
- Lab 1: Automating test case creation with AI
- Lab 2: Developing a defect prediction model from historical test data
- Lab 3: Utilizing LLMs to audit and refine test scripts
- Capstone: End-to-end deployment of an AI-powered testing pipeline
Requirements
Prospective participants are expected to possess:
- At least two years of professional experience in software testing or QA roles.
- Working knowledge of test automation frameworks (such as Selenium, JUnit, or Cypress).
- Fundamental programming proficiency, ideally in Python or JavaScript.
- Practical experience with version control systems and CI/CD tools (including Git and Jenkins).
- No prior background in AI or ML is necessary, although a strong curiosity and a readiness to experiment are highly valued.
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.
