Course Outline
Module 1: Introduction to Artificial Intelligence for Quality Assurance (QA)
- Definition and scope of Artificial Intelligence (AI)
- Comparison of Machine Learning, Deep Learning, and Rule-based Systems
- The evolution of software testing methodologies with the integration of AI
- Key benefits and challenges of implementing AI in QA for government
Module 2: Data and Machine Learning Fundamentals for Testers
- Understanding the differences between structured and unstructured data
- Explanation of features, labels, and training datasets in machine learning
- Overview of supervised and unsupervised learning techniques
- Introduction to model evaluation metrics (accuracy, precision, recall, etc.)
- Examples of real-world QA datasets for government applications
Module 3: AI Applications in Quality Assurance
- AI-driven generation of test cases
- Predictive modeling for defect detection using machine learning
- Strategies for test prioritization and risk-based testing with AI
- Visual testing techniques utilizing computer vision
- Log analysis and anomaly detection in QA processes
- Utilizing natural language processing (NLP) to enhance test scripts
Module 4: AI Tools for Quality Assurance
- Overview of AI-enabled platforms designed for QA
- Using open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras) to develop QA prototypes
- Introduction to large language models (LLMs) in test automation for government
- Step-by-step guide to building a simple AI model for predicting test failures
Module 5: Integrating AI into Quality Assurance Workflows
- Assessing the AI-readiness of QA processes in government agencies
- Embedding intelligence into continuous integration and delivery (CI/CD) pipelines
- Designing intelligent test suites for enhanced efficiency and accuracy
- Managing model drift and retraining cycles to maintain AI effectiveness
- Ethical considerations in the deployment of AI-powered testing solutions
Module 6: Hands-on Labs and Capstone Project
- Lab 1: Automate test case generation using AI techniques
- Lab 2: Develop a defect prediction model utilizing historical test data
- Lab 3: Utilize an LLM to review and optimize test scripts for government applications
- Capstone Project: Implement an end-to-end AI-powered testing pipeline in a real-world scenario
Requirements
Testimonials (4)
The instructor's teaching style was very good.
Kubra
Course - Automation Testing using Selenium
The patience and pace of the lecturer.
Jace - Vodacom
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.
Lorenz - Continentale Lebensversicherung AG
Course - Advanced Selenium
I gained new knowledge and I'm pretty confident about it. Nothing unclear.
