Master Generative AI for Software Testing: Manual to Auto
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Unclaimed ProfileThe landscape of software quality assurance is shifting rapidly, and "Master Generative AI for Software Testing: Manual to Auto" by Gianni Bruno is designed to help both manual and automation testers stay ahead of the curve. This comprehensive course bridges the gap between traditional testing methodologies and modern AI-driven automation. Students will dive deep into integrating Generative AI tools, such as ChatGPT and the OpenAI API, into their daily testing workflows. The curriculum covers how to dynamically create, analyze, and refine test plans, test cases, and Behavior-Driven Development (BDD) scenarios. Beyond conceptual AI integration, this course offers robust technical training in Python, Playwright, and Behave BDD frameworks. Testers will learn how to build AI-enhanced frameworks for API and UI testing, ensuring dynamic and efficient test execution. Crucial real-world practices are also addressed, including setting up continuous integration pipelines using Jenkins, handling test data privacy through anonymization, and generating detailed test reports via Allure. Whether you are a manual tester looking to transition into automation or an experienced automation engineer aiming to optimize your test creation and refinement using state-of-the-art AI tools, this course provides the practical, hands-on experience needed to thrive in the modern tech ecosystem.
About the creator
Gianni Bruno
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Gianni Bruno is a dedicated software quality assurance expert who has spent his career bridging the gap between traditional manual testing workflows and the high-efficiency demands of modern, AI-driven development environments. With a focus on practical application rather than purely theoretical instruction, Gianni has helped numerous developers and testers overhaul their legacy processes. His methodology is centered on the belief that generative AI is not a replacement for human intuition in software quality assurance, but rather a powerful force multiplier that can eliminate repetitive drudgery. Throughout his tenure in the tech industry, he has navigated the shifting landscapes of automation…Show more
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Program Overview
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Learning format
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Price
Price may change · updated within 1–2 weeks
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What You'll Learn
- Use Generative AI tools like ChatGPT and OpenAI API to create, analyze, and refine test plans, cases, and BDD scenarios dynamically.
- Master Python essentials: variables, functions, file handling, and libraries like Requests for effective automation testing.
- Build AI-enhanced frameworks using Behave BDD for API and Playwright for UI testing, focusing on dynamic and efficient test creation.
- Optimize automation tests with AI: refine BDD steps, generate test reports, automate cleanup, and integrate advanced features.
- Configure Jenkins CI pipelines to execute automated UI and API tests, generate reports, and streamline test workflows seamlessly.
- Ensure test data privacy: anonymize sensitive data, use environment variables, and follow ethical AI best practices with ChatGPT.
- Gain hands-on experience with real-world projects: automate Contact Us, Login APIs, and Goal Tracker API using practical exercises.
- Learn advanced testing techniques like custom test runners, targeted execution with tags, and detailed reporting using Allure.
Best For
- Manual testers seeking a practical roadmap to transition into automated quality assurance roles.
- Existing test automation engineers looking to integrate LLMs and Generative AI into their existing workflows.
- QA professionals who want to leverage AI for rapid test case generation and BDD scenario creation.
- Engineers interested in building robust, AI-supported testing frameworks using Python, Playwright, and Behave.
Not For
- Software developers looking for comprehensive backend application development training rather than testing automation.
- Absolute beginners with no interest in programming or foundational software testing concepts.
- Individuals seeking theoretical AI research or machine learning model training rather than applied AI tools for testing.
- Automation engineers exclusively committed to legacy tools who have no interest in adopting AI-augmented workflows.
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