Masterclass Software Quality Engineering | AI Testing

Overview

Quality Engineering, Modern Testing Principles, Devops for Quality Engineers, Automation Testing, Generetive AI - GPT What you'll learn:

Learn modern ways to look at quality for software projects that are consistent with todays principlesApply continuous improvement techniques to increase speed and qualityAgentic Testing with Microsoft AutogenAgentic Testing with Flowise AIUnderstand how to use Artificial Intelligence to gain a productivity boost as well as help create powerful artifactsAgentic Testing with Playwright MCP and GithubSee how to rapid start a project by leveraging ChatGPT to deploy a Playwright frontend automation frameworkUse CHAT GPT to Create automated Performance Test PipelinesUnderstand how Shift Left on quality increases release cadence, prevents defects and improves qualityIdentify pitfalls in the development process and their corrective actionsLeverage the power of Continuous Testing, Development and testing with proper tool setup and guidance of framework implementationUnderstand how Quality engineering principles are applied to agile deliveries to achieve in sprint automation and on time deliveryUnderstand how to create your own AI Agents for TestingHow to choose the right suited DEVOPS Testing pipelineUnderstand and improve the testing process as an expert, AI cannot teach you thisUnderstand how test automation supports the overall quality approach and see some best practice to increase speed and reliability of executionMaster the use of the Test Pyramid :

unit, integration, UI testsUnderstand how to adapt your testing approach depending on the branching strategy that you haveUnderstand how to create on Demand/Feature Environment and how they are used in Continuous Testing and Shift Left ApproachUnderstand how LEAN principles apply to software development and gain know how on how to build a Value Stream Map to identity bottlenecksWe will understand the 8 Wastes of TIMWOODS that affect software delivery teams Unique on the Internet!This course is designed for software developers, testers, quality engineers, DevOps professionals, and anyone interested in improving software quality through modern testing strategies and tools. Whether you’re a beginner seeking foundational knowledge or an experienced professional looking to adopt the latest techniques, this course provides actionable insights and practical applications to help you stay ahead in today’s fast-paced software development landscape.A huge number of laboratories and hands on activities await you in this material.

By the end of this course, you will have mastered the tools, techniques, and principles needed to deliver high-quality software projects efficiently. Let’s take a closer look at what you’ll learn:

Modern Approaches to Software QualityUnderstand how to align quality strategies with today’s industry standards to ensure success in modern software projects.Continuous Improvement TechniquesLearn how to apply continuous improvement methods to increase speed and quality throughout the software development lifecycle.Leveraging Artificial Intelligence (AI) in TestingDiscover how to use AI tools to boost productivity, automate processes, and gain a competitive edge in quality assurance.Kickstarting Projects with ChatGPTExplore how to use ChatGPT to deploy automated frontend testing using Playwright and start projects quickly and efficiently.Automating Performance Test PipelinesLearn to design and implement automated performance testing pipelines that provide actionable insights into system bottlenecks.Shift-Left Testing ApproachUnderstand how adopting a "Shift Left" mindset improves release cadence, prevents defects, and enhances overall quality.Identifying and Addressing PitfallsGain the skills to spot common development challenges and implement corrective actions for seamless project delivery.Continuous Testing in DevOpsMaster the integration of testing into a DevOps pipeline with the proper tools and strategies for continuous testing and delivery.Quality Engineering for Agile DeliveriesLearn how quality engineering principles are applied to agile methodologies to achieve consistent and reliable results.Building AI Agents for TestingDiscover how to create your own AI-driven testing agents to automate and streamline repetitive testing tasks.Choosing the Right DevOps Testing PipelineGet guidance on selecting and implementing the most suitable DevOps testing pipeline for your organization’s needs.Improving the Testing ProcessUnderstand the key factors that go into a successful testing process, focusing on areas where AI cannot substitute human expertise.Test Automation and Its Role in QualityExplore how automation supports the overall quality strategy and review best practices for implementation.Mastering the Test PyramidLearn the importance of unit, integration, and UI tests within the Test Pyramid framework and how to balance your testing strategy.Adapting Testing to Branching StrategiesDiscover how to align your testing approach with various branching strategies to enhance development workflows.On-Demand/Feature Environments in CI/CDUnderstand how to create on-demand and feature-specific environments and use them effectively in Continuous Integration/Continuous Delivery.LEAN Principles in Software DevelopmentLearn how LEAN principles can be applied to software development to reduce waste and improve efficiency.The 8 Wastes of TIMWOODSIdentify the common inefficiencies in software delivery and gain practical tips on how to eliminate them.This course combines theoretical knowledge with hands-on examples to help you build a robust skill set.

Join now and take your testing and quality engineering expertise to the next level!

Syllabus

• Introduction to Software Quality Engineering
Overview of Software Quality Principles
Importance of Testing in AI Systems
• Fundamentals of AI Systems
Understanding AI Models and Algorithms
Key Differences Between Traditional Software and AI Systems
• Testing Strategies for AI Applications
Test Planning and Design for AI
Understanding Data Dependencies in AI Testing
Handling Uncertainty and Probabilistic Outputs
• Types of Testing in AI
Functional Testing of AI Components
Non-functional Testing: Usability, Performance, and Security
Black Box vs White Box Testing in AI
• Automation in AI Testing
Introduction to Testing Automation Tools for AI
Creating and Managing Automated Test Suites
• Testing Machine Learning Models
Techniques for Validating Model Accuracy
Addressing Bias and Fairness Testing
Overfitting and Underfitting Challenges
• Testing AI in Production
Monitoring AI Performance Post-Deployment
Continuous Testing and Feedback Loops
• Ethical Considerations in AI Testing
Privacy and Data Governance
Ensuring Transparency and Explainability
• Case Studies and Real-world Applications
Analysis of Industry-specific AI Testing Scenarios
Lessons Learned from AI Testing Failures
• Tools and Resources for AI Testing
Overview of Popular AI Testing Frameworks
Resources for Continuous Learning in AI Testing
• Course Conclusion
Summary of Key Learnings
Future Trends in AI and Software Quality Engineering

Taught by

Dan Andrei Bucureanu

Subjects

Programming