Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
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
Participants for government roles are expected to have:
- A minimum of 2 years of experience in software testing or quality assurance positions.
- Familiarity with test automation tools such as Selenium, JUnit, or Cypress.
- Basic knowledge of programming, preferably in Python or JavaScript.
- Experience with version control systems and CI/CD tools, including Git and Jenkins.
- No prior experience in artificial intelligence or machine learning is required; however, a strong curiosity and willingness to experiment are essential.
21 Hours
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
The instructor's teaching style was very good.
