Schulungsübersicht
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
Voraussetzungen
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
Erfahrungsberichte (5)
Ich habe alles genossen, denn es ist alles neu für mich, und ich kann den Mehrwert erkennen, den es für meine Arbeit bedeuten kann.
Zareef - BMW South Africa
Kurs - Tosca: Model-Based Testing for Complex Systems
Maschinelle Übersetzung
Sehr umfassender Überblick über das Thema, der alle notwendigen Vorkenntnisse auf eine für das Kurswissen angemessene Art und Weise abdeckt.
James Hurburgh - Queensland Police Service
Kurs - SpecFlow: Implementing BDD for .NET
Maschinelle Übersetzung
Es war einfach zu verstehen und umzusetzen.
Thomas Young - Canadian Food Inspection Agency
Kurs - Robot Framework: Keyword Driven Acceptance Testing
Maschinelle Übersetzung
Anzahl der praktischen Übungen.
Jakub Wasikowski - riskmethods sp. z o.o
Kurs - API Testing with Postman
Maschinelle Übersetzung
Um sich mit dem Screenplay-Muster vertraut zu machen und zu lernen, warum dieses Muster besser ist als das alte Muster.
Peter Moors
Kurs - Serenity BDD for Automated Acceptance Tests
Maschinelle Übersetzung