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Course Outline
Module 1: Introduction to AI for QA
- What is Artificial Intelligence?
- Machine Learning vs Deep Learning vs Rule-based Systems
- The evolution of software testing with AI
- Key benefits and challenges of AI in QA
Module 2: Data and ML Basics for Testers
- Understanding structured vs unstructured data
- Features, labels, and training datasets
- Supervised and unsupervised learning
- Intro to model evaluation (accuracy, precision, recall, etc.)
- Real-world QA datasets
Module 3: AI Use Cases in QA
- AI-powered test case generation
- Defect prediction using ML
- Test prioritization and risk-based testing
- Visual testing with computer vision
- Log analysis and anomaly detection
- Natural language processing (NLP) for test scripts
Module 4: AI Tools for QA
- Overview of AI-enabled QA platforms
- Using open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras) for QA prototypes
- Introduction to LLMs in test automation
- Building a simple AI model to predict test failures
Module 5: Integrating AI into QA Workflows
- Evaluating AI-readiness of your QA processes
- Continuous integration and AI: how to embed intelligence into CI/CD pipelines
- Designing intelligent test suites
- Managing AI model drift and retraining cycles
- Ethical considerations in AI-powered testing
Module 6: Hands-on Labs and Capstone Project
- Lab 1: Automate test case generation using AI
- Lab 2: Build a defect prediction model using historical test data
- Lab 3: Use an LLM to review and optimize test scripts
- Capstone: End-to-end implementation of an AI-powered testing pipeline
Requirements
Participants are expected to have:
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2+ years experience in software testing/QA roles
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Familiarity with test automation tools (e.g., Selenium, JUnit, Cypress)
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Basic knowledge of programming (preferably in Python or JavaScript)
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Experience with version control and CI/CD tools (e.g., Git, Jenkins)
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No prior AI/ML experience required, though curiosity and willingness to experiment are essential
21 Hours
Testimonials (5)
The exercises we saw in the course were quite useful and applicable to my activities at work. My questions were answered and the examples shared are quite useful.
jocelin salas - BANXICO
Course - Test Automation with Selenium and Python
Machine Translated
I enjoyed everything as it is all new for me and I can see the added value it can ring to my work.
Zareef - BMW South Africa
Course - Tosca: Model-Based Testing for Complex Systems
The Dynamics.
Cesar Ortiz Lara - Bienes Programados SA de CV
Course - Selenium WebDriver in C#
Machine Translated
Many exercises, which gave a lot of practical skills.
Rafal Borek - Bytamic Solutions sp. z o.o.
Course - Automation Testing with Cypress
Amount of hands-on excersises.
