Course Outline
Module 1: Introduction to AI for QA
- Overview of Artificial Intelligence
- Differentiating Machine Learning, Deep Learning, and Rule-based Systems
- The evolution of software testing through the integration of AI
- Key benefits and challenges of incorporating AI in quality assurance (QA)
Module 2: Data and ML Fundamentals for Testers
- Understanding structured versus unstructured data
- Exploring features, labels, and training datasets
- Supervised and unsupervised learning methodologies
- Introduction to model evaluation metrics (accuracy, precision, recall, etc.)
- Examination of real-world QA datasets for government use
Module 3: AI Applications in QA
- AI-driven test case generation
- Predictive defect analysis using machine learning
- Test prioritization and risk-based testing strategies
- Visual testing with computer vision techniques
- Log analysis and anomaly detection methods
- Natural language processing (NLP) for test script optimization
Module 4: AI Tools for QA
- Overview of AI-enabled QA platforms for government use
- Utilizing open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras) for QA prototyping
- Introduction to large language models (LLMs) in test automation
- Constructing a simple AI model to predict test failures
Module 5: Integrating AI into QA Workflows for Government
- Assessing the AI-readiness of your QA processes for government applications
- Continuous integration and AI: embedding intelligence into CI/CD pipelines for government projects
- Designing intelligent test suites for government systems
- Managing AI model drift and retraining cycles in a government context
- Ethical considerations in AI-powered testing for government
Module 6: Hands-on Labs and Capstone Project for Government
- Lab 1: Automate test case generation using AI for government applications
- Lab 2: Build a defect prediction model using historical test data for government systems
- Lab 3: Use an LLM to review and optimize test scripts for government use
- Capstone: End-to-end implementation of an AI-powered testing pipeline for government projects
Requirements
Participants for government are expected to have:
- A minimum of 2 years of experience in software testing or quality assurance roles
- Familiarity with test automation tools, such as Selenium, JUnit, and Cypress
- Basic knowledge of programming, preferably in Python or JavaScript
- Experience with version control systems and CI/CD tools, such as Git and Jenkins
- No prior experience in artificial intelligence or machine learning is required; however, a strong curiosity and willingness to experiment are essential
Testimonials (5)
The instructor's teaching style was very good.
Kubra
Course - Automation Testing using Selenium
Good rapport, Łukasz had time for everyone's questions and was able to help anyone who had any issue
Kelly Morris - Titian Software Poland Sp. z o.o.
Course - Selenium WebDriver in C#
Amount of hands-on excersises.
Jakub Wasikowski - riskmethods sp. z o.o
Course - API Testing with Postman
The trainer explained every functionality thoroughly.
Argean Quilaquil - DXC
Course - TestComplete
Trainer is nice. His explanation is clear and interesting. He try to make the lessons interesting as possible. I enjoyed the lesson and gained a lot of knowledge. Thank you so much. The most useful technique I learned is the locating elements for different web component like textbox, radio buttons and buttons. Sometimes, the element ID is not capture correctly. We learned a different way of locating elements by using CSS selectors, XPath, Name and ID. I like the explanation. Thanks
