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Duration 21 hours
Course Outline
Introduction to AI for QA
- Defining Artificial Intelligence.
- Distinguishing between Machine Learning, Deep Learning, and Rule-based Systems.
- Tracing the evolution of software testing in the era of AI.
- Examining the primary advantages and obstacles of AI in QA.
Data and ML Fundamentals for Testers
- Interpreting structured versus unstructured data.
- Exploring features, labels, and training datasets.
- Comparing supervised and unsupervised learning approaches.
- Familiarizing with model evaluation metrics (accuracy, precision, recall, etc.).
- Reviewing real-world QA datasets.
AI Applications in QA
- Generating test cases with AI.
- Predicting defects using ML techniques.
- Implementing test prioritization and risk-based testing.
- Conducting visual testing via computer vision.
- Analyzing logs and detecting anomalies.
- Applying NLP to streamline test scripts.
AI Toolset for QA
- Surveying AI-enabled QA platforms.
- Utilizing open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras) to build QA prototypes.
- Exploring the role of LLMs in test automation.
- Constructing a basic AI model for predicting test failures.
Integrating AI into QA Workflows
- Assessing the AI-readiness of current QA processes.
- Embedding intelligence into CI/CD pipelines through continuous integration and AI.
- Architecting intelligent test suites.
- Overseeing AI model drift and managing retraining cycles.
- Navigating the ethical implications of AI-powered testing.
Practical Labs and Capstone Project
- Lab 1: Automating test case generation with AI.
- Lab 2: Creating a defect prediction model from historical test data.
- Lab 3: Employing an LLM to review and refine test scripts.
- Capstone: Delivering a complete, end-to-end AI-powered testing pipeline.
Requirements
Prospective participants should possess:
- At least two years of hands-on experience in software testing or QA positions.
- Proficiency with test automation frameworks (such as Selenium, JUnit, or Cypress).
- A working knowledge of programming languages, ideally Python or JavaScript.
- Practical exposure to version control and CI/CD systems (e.g., Git, Jenkins).
- No previous AI/ML background is necessary; however, a strong sense of curiosity and a readiness to experiment are vital.
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.
