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Duration 21 hours
Course Outline
Introduction to AI for QA
- The definition and scope of Artificial Intelligence.
- Distinguishing Machine Learning, Deep Learning, and Rule-based Systems.
- The evolution of software testing in the age of AI.
- Key advantages and challenges associated with AI in QA.
Data and ML Basics for Testers
- Understanding the distinction between structured and unstructured data.
- Exploring features, labels, and training datasets.
- Overview of supervised and unsupervised learning methods.
- Introduction to model evaluation metrics (accuracy, precision, recall, etc.).
- Analysis of real-world QA datasets.
AI Use Cases in QA
- Generating test cases using AI.
- Utilising ML for defect prediction.
- Test prioritisation and risk-based testing strategies.
- Implementing visual testing via computer vision.
- Log analysis and anomaly detection.
- Applying Natural Language Processing (NLP) to test scripts.
AI Tools for QA
- Overview of leading AI-enabled QA platforms.
- Creating QA prototypes using open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras).
- Introduction to the role of LLMs in test automation.
- Developing a basic AI model to forecast test failures.
Integrating AI into QA Workflows
- Assessing the AI-readiness of current QA processes.
- Integrating intelligence into CI/CD pipelines through continuous integration.
- Designing intelligent test suites.
- Managing AI model drift and retraining cycles.
- Ethical considerations in AI-driven testing.
Hands-on Labs and Capstone Project
- Lab 1: Automating test case generation with AI.
- Lab 2: Constructing a defect prediction model from historical test data.
- Lab 3: Leveraging an LLM to review and optimise test scripts.
- Capstone: End-to-end implementation of an AI-powered testing pipeline.
Requirements
Participants are expected to possess the following qualifications:
- At least two years of experience in software testing or QA roles.
- Proficiency with test automation tools (e.g., Selenium, JUnit, Cypress).
- A basic grasp of programming languages, preferably Python or JavaScript.
- Familiarity with version control and CI/CD tools (e.g., Git, Jenkins).
- While prior AI/ML experience is not mandatory, a strong curiosity and readiness to experiment are essential.
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customised Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from £4800 + VAT*
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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.
