AGM-AIEST: AI-Enabled Software Testing Training Course

Module 1: Introduction to Agile Testing with AI

This module covers the basics of Agile Testing and how AI enhances speed, accuracy, and defect prediction. Learn why AI is essential in Agile Testing, its key benefits like self-healing tests and predictive defect analysis, and the challenges of traditional testing that AI helps overcome.

Module 2: Agile Testing Fundamentals

This module explores Agile Testing principles, the Agile Testing Quadrants, and effective testing strategies in Scrum and Kanban. It also highlights the role of testers in Agile teams, ensuring seamless collaboration and quality-driven development.

Module 3: AI in Agile Testing: An Overview

This module explores how AI transforms software testing by improving speed, accuracy, and automation. Learn about different AI-driven testing types like ML-based, NLP-powered, and predictive analytics. Understand the differences between AI-based and traditional test automation and explore powerful AI tools like Testim, Functionize, and Mabl for automated and efficient testing.

Module 4: Test-Driven Development (TDD) and AI

This module explores TDD in Agile development, its role in improving code quality, and how AI enhances test case generation. Learn how AI-driven tools assist in code analysis, automated test creation, and feedback loops, making TDD faster and more efficient. Hands-on experience with Diffblue Cover and CodiumAI will demonstrate AI-powered unit testing and smart code suggestions.

Module 5: Behavior-Driven Development (BDD) and AI

This module explains BDD and its differences from TDD, focusing on how AI enhances BDD workflows. Learn how AI assists in Gherkin syntax generation, automates feature file execution, and uses NLP to convert requirements into automated tests. Explore AI tools like Cucumber with AI plugins for NLP-based test generation and TestCraft for AI-powered BDD testing.

Module 6: AI-Driven Test Automation

This module explores how AI enhances test automation with self-healing capabilities, reducing maintenance efforts. Learn how AI generates and maintains test scripts, automates test execution and reporting, and enables continuous testing in CI/CD pipelines. Get hands-on experience with tools like Applitools, Katalon Studio, and Selenium with AI plugins to streamline testing workflows.

Module 7: AI-Powered Test Data Management

This module explores how AI enhances test data management by generating synthetic test data, improving test coverage, and ensuring data privacy. Learn how AI automates smart test data selection, reduces redundancy, and strengthens security. Hands-on experience with Tonic.ai and Datomize will help in optimizing test data for Agile environments.

Module 8: AI for Defect Prediction and Prevention

This module explores how machine learning models predict defects, helping teams identify issues before they occur. Learn how AI-driven root cause analysis speeds up debugging and how intelligent test prioritization ensures high-risk areas get tested first. Get hands-on with tools like Launchable for risk-based test selection and SonarQube with AI for code quality checks

Module 9: AI-Driven Performance and Security Testing

This module explores how AI improves performance and security testing by enabling automated load testing, vulnerability detection, and real-time monitoring. Learn how AI-driven tools like Neotys Neoload optimize performance testing, and Darktrace enhances security with AI-powered anomaly detection.

Module 10: AI for Exploratory and Visual Testing

This module explores how AI enhances exploratory testing by identifying defects faster and improving test coverage. Learn about image recognition-based UI testing for visual validation and AI-driven accessibility testing to ensure inclusive design. Hands-on experience with tools like Testim for AI-powered exploratory testing and Applitools for visual testing will be included.

Module 11: AI in Test Management and Decision-Making

This module explores how AI optimizes test management by automating test planning, case selection, and strategy execution. Learn how predictive analytics improve test coverage and efficiency, reducing manual effort. Discover AI-powered tools like Xray and PractiTest that enhance decision-making and analytics for smarter testing.

Module 12: Challenges and Considerations in AI-Powered Agile Testing

This module explores key challenges in AI-driven testing, including AI model accuracy, reliability, and potential biases that can impact test outcomes. It also covers ethical concerns, ensuring fair and unbiased testing. Additionally, participants will learn about selecting the right AI tools and effectively integrating them into Agile workflows for seamless automation.

Module 13: Case Studies and Industry Adoption

This module explores real-world applications of AI-powered Agile Testing in industries like banking and fintech. It includes case studies on AI-driven test automation, showcasing its impact on speed, accuracy, and defect detection. Participants will learn from practical examples, key challenges, and best practices for successful AI adoption in Agile Testing.

Module 14: Future of AI in Agile Testing

This module explores the latest trends in AI-powered testing, including self-healing automation, predictive analytics, and AI-driven test management. Understand how generative AI is transforming software testing by automating test creation, improving defect detection, and enhancing test coverage. Learn how AI will reshape Agile Testing methodologies for faster, smarter, and more efficient testing processes.