AiU-Certified GenAI-Assisted Test Engineer

Description

AiU Certified GenAI-Assisted Test Engineer (GenAiA-TE)

As pioneers in the integration of Artificial Intelligence (AI) within testing frameworks, we at Artificial Intelligence United (AiU) are thrilled to unveil our latest offering: the Certified GenAI-Assisted Testing Engineer (GenAiA-TE) course! In just three days, you'll embark on a journey through seven comprehensive chapters, each meticulously designed to equip you with cutting-edge knowledge and practical skills:

•  GenAI-Assisted Testing: Discover the evolution of testing from manual to AI-assisted methodologies. Learn the inner workings of Language Model-based AI, its benefits in efficiency and human-guided testing, and how to overcome challenges in its implementation.
• Prompt Engineering: Master the art of crafting effective prompts, essential for guiding AI in generating high-quality test cases. Adapt to changes seamlessly and maintain quality standards with insights from seasoned professionals.
• Requirements Review: Capturing requirements is a critical step in a development life cycle, as this is where one or more individuals try to express, usually a vague notion of what is to be built in a recorded format. There can be many issues in this translation of what is supposed to be built (which could have its own issues in understanding) versus what is captured. Requirement review is a stage for one of the earliest forms of testing where testers can contribute to improving the requirements. Given the inherent complexity of requirement reviews, AI can greatly assist a tester.
• Test Generation and Optimization: Explore diverse aspects of test design, leveraging AI efficiently, experimenting with formats, and incorporating systematic techniques. Unleash your creativity with open-ended test idea exploration while considering technology and non-functional testing factors.
• Test Data: Harness the power of AI for data representation and generation, mastering techniques like regular expressions to manipulate data effectively in various formats.
• Bug Advocacy and Reporting: Large Language Models built on Natural Language Processing are especially powerful at generating and evaluating textual content in various formats. A tester can use this power to advocate bugs better by writing reports that sell.
• Further Steps: Beyond the scope of this course, there are still many current and future possibilities for AI assistance in testing. This concluding section gives a glimpse of these opportunities. It also shows how AI is not a full replacement for human intellect and how, in combination, these two forms of intelligence can do magic.

Why Enroll?

• Stay Ahead: Position yourself at the forefront of the testing field by integrating AI into your skillset.
• Practical Skills: Gain hands-on experience with real-world projects and scenarios that prepare you for immediate industry impact. 90% of the training is hands-on.
• Expert Instructors: Learn from leaders in the field, bringing you the latest insights and techniques from the world of AI testing.