Gracias por enviar su consulta! Uno de los miembros de nuestro equipo se pondrá en contacto con usted en breve.
Gracias por enviar su reserva! Uno de los miembros de nuestro equipo se pondrá en contacto con usted en breve.
Programa del Curso
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
Requerimientos
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
21 Horas
Testimonios (5)
Método de enseñanza
Negritu - OMNIASIG VIENNA INSURANCE GROUP S.A.
Curso - SoapUI for API Testing
Traducción Automática
Disfruté todo, ya que todo es nuevo para mí y puedo ver el valor agregado que puede sonar a mi trabajo.
Zareef - BMW South Africa
Curso - Tosca: Model-Based Testing for Complex Systems
Traducción Automática
Muy amplio resumen del tema que abarcó todo el conocimiento previo de manera apropiada para el nivel del curso.
James Hurburgh - Queensland Police Service
Curso - SpecFlow: Implementing BDD for .NET
Traducción Automática
Fue fácil de entender y de implementar.
Thomas Young - Canadian Food Inspection Agency
Curso - Robot Framework: Keyword Driven Acceptance Testing
Traducción Automática
Cantidad de ejercicios prácticos.
Jakub Wasikowski - riskmethods sp. z o.o
Curso - API Testing with Postman
Traducción Automática
