Masterclass en Qualité Logicielle | Tests d'IA

Aperçu

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!

Programme

• Introduction à l'ingénierie de la qualité logicielle
Aperçu des principes de qualité logicielle
Importance des tests dans les systèmes d'IA
• Fondamentaux des systèmes d'IA
Compréhension des modèles et algorithmes d'IA
Différences clés entre les logiciels traditionnels et les systèmes d'IA
• Stratégies de test pour les applications d'IA
Planification et conception de tests pour l'IA
Compréhension des dépendances de données dans les tests d'IA
Gestion de l'incertitude et des résultats probabilistes
• Types de tests dans l'IA
Tests fonctionnels des composants d'IA
Tests non fonctionnels : utilisabilité, performance et sécurité
Test en boîte noire vs test en boîte blanche dans l'IA
• Automatisation dans les tests d'IA
Introduction aux outils d'automatisation des tests pour l'IA
Création et gestion de suites de tests automatisés
• Tests de modèles d'apprentissage automatique
Techniques de validation de la précision des modèles
Prise en compte des biais et tests d'équité
Défis de surajustement et de sous-ajustement
• Tests de l'IA en production
Surveillance de la performance de l'IA après le déploiement
Tests continus et boucles de rétroaction
• Considérations éthiques dans les tests d'IA
Protection de la vie privée et gouvernance des données
Garantie de transparence et d'explicabilité
• Études de cas et applications réelles
Analyse de scénarios de tests d'IA spécifiques à l'industrie
Leçons tirées des échecs de tests d'IA
• Outils et ressources pour les tests d'IA
Aperçu des cadres de tests d'IA populaires
Ressources pour l'apprentissage continu en tests d'IA
• Conclusion du cours
Résumé des enseignements clés
Tendances futures dans l'IA et l'ingénierie de la qualité logicielle

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Enseigné par

Dan Andrei Bucureanu

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Matières

Programming