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Duration 21 hours
Course Outline
Introduction to AI in QA
- Defining Artificial Intelligence.
- Distinguishing between Machine Learning, Deep Learning, and Rule-based Systems.
- The evolution of software testing through AI integration.
- Key advantages and challenges associated with AI in QA.
Data and ML Fundamentals for Testers
- Differentiating between structured and unstructured data.
- Understanding features, labels, and training datasets.
- Exploring supervised and unsupervised learning models.
- Introductory insights into model evaluation metrics (accuracy, precision, recall, etc.).
- Examining real-world QA datasets.
AI Applications in QA
- Generating test cases powered by AI.
- Utilizing ML for defect prediction.
- Optimizing test prioritization and risk-based testing.
- Employing computer vision for visual testing.
- Performing log analysis and anomaly detection.
- Leveraging Natural Language Processing (NLP) for test scripting.
AI Tools for QA
- Overview of AI-enabled QA platforms.
- Utilizing open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras) to build QA prototypes.
- Introduction to LLMs in the context of test automation.
- Constructing a basic AI model to forecast test failures.
Embedding AI into QA Workflows
- Assessing the AI-readiness of existing QA processes.
- Integrating AI with continuous integration: embedding intelligence into CI/CD pipelines.
- Designing intelligent and adaptive test suites.
- Managing AI model drift and retraining cycles.
- Navigating ethical considerations in AI-powered testing.
Practical Labs and Capstone Project
- Lab 1: Automating test case generation using AI.
- Lab 2: Constructing a defect prediction model based on historical test data.
- Lab 3: Utilizing an LLM to review and optimize test scripts.
- Capstone: Comprehensive implementation of an AI-powered testing pipeline.
Requirements
Prospective participants should meet the following expectations:
- Minimum of two years of experience in software testing or QA positions.
- Proficiency with test automation frameworks such as Selenium, JUnit, and Cypress.
- Fundamental programming knowledge, ideally in Python or JavaScript.
- Working experience with version control and CI/CD tools like Git and Jenkins.
- No previous background in AI/ML is necessary, although a curious mindset and 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
The instructor's teaching style was very good.
