AI Enablement Training Program for Software QA Engineers

Participants are expected to have:

• 2+ years experience in software testing/QA roles
• Familiarity with test automation tools (e.g., Selenium, JUnit, Cypress)
• Basic knowledge of programming (preferably in Python or JavaScript)
• Experience with version control and CI/CD tools (e.g., Git, Jenkins)
• No prior AI/ML experience required, though curiosity and willingness to experiment are essential

This training program is designed to empower software QA engineers with essential AI knowledge and hands-on skills to enhance their testing productivity and software quality outcomes. Participants will gain a foundational understanding of artificial intelligence, machine learning, and their application in test automation, bug detection, test case generation, risk-based testing, and performance analysis.

Through a mix of theory, demos, and practical labs, QA professionals will learn how to leverage AI to reduce manual efforts, improve test coverage, and increase defect detection accuracy—thereby driving overall efficiency and effectiveness in QA processes

By the end of this course, participants will be able to:

• Understand the foundational concepts of AI and ML relevant to software testing.
• Identify areas in the QA lifecycle that can benefit from AI-powered tools and techniques.
• Use AI models for tasks such as test case generation, defect prediction, and risk analysis.
• Evaluate and integrate AI-based testing tools into existing QA workflows.
• Collaborate with data scientists and engineers to design and implement intelligent QA strategies.

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