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Duration 21 hours
Course Outline
Foundations of AI in QA
- The basics of Artificial Intelligence
- Distinguishing between Machine Learning, Deep Learning, and Rule-based Systems
- The progression of software testing driven by AI
- Primary advantages and potential challenges of AI in QA
Essential Data and ML Concepts for Testers
- Differentiating between structured and unstructured data
- Understanding features, labels, and training datasets
- Overview of Supervised and Unsupervised Learning
- Introduction to model assessment metrics (accuracy, precision, recall, etc.)
- Analysis of real-world QA datasets
Practical AI Applications in QA
- Automating test case generation with AI
- Predicting defects using ML techniques
- Optimizing test prioritization and risk-based strategies
- Implementing visual testing via computer vision
- Performing log analysis and anomaly detection
- Applying NLP for automated test scripts
AI Tools and Technologies for QA
- Survey of AI-enabled QA platforms
- Leveraging open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras) for QA prototypes
- Role of LLMs in test automation
- Creating a basic AI model to forecast test failures
Embedding AI into QA Workflows
- Assessing the AI-readiness of current QA processes
- Integrating AI into CI/CD pipelines for continuous intelligence
- Architecting intelligent test suites
- Handling AI model drift and managing retraining schedules
- Ethical implications of AI-driven testing
Practical Labs and Capstone Exercise
- Lab 1: Automating test case creation with AI
- Lab 2: Developing a defect prediction model from historical test data
- Lab 3: Utilizing LLMs to audit and refine test scripts
- Capstone: End-to-end deployment of an AI-powered testing pipeline
Requirements
Prospective participants are expected to possess:
- At least two years of professional experience in software testing or QA roles.
- Working knowledge of test automation frameworks (such as Selenium, JUnit, or Cypress).
- Fundamental programming proficiency, ideally in Python or JavaScript.
- Practical experience with version control systems and CI/CD tools (including Git and Jenkins).
- No prior background in AI or ML is necessary, although a strong curiosity and a readiness to experiment are highly valued.
Testimonials (1)
The most valuable takeaway was learning how to effectively use AI to enhance testing activities, particularly through better prompt engineering, AI agents, RAG, and AI-assisted test analysis.
