STARWEST 2024 - AI/ML
Wednesday, September 25
The Rise of the Virtual QA Engineer: Harnessing GenAI
In this talk, Dmitriy will delve into the transformative journey of integrating GenAI, into the core of his team's testing and development processes. This integration has not only enhanced their productivity by 15% but also yielded a 20% time saving and a significant cost reduction per test case. Want to know how? The session will explore his team's strategic implementation of GenAI, overcoming security challenges, leveraging diverse (LLMs), and the meticulous design of prompts that culminated in a prompt library. See how tailored extensions for the code editors and corporate chats...
Thursday, September 26
Bridging AI-Generated Acceptance Criteria with Comprehensive Test Scenarios
The generation of acceptance criteria through generative AI is an innovative approach to streamline the requirements gathering process. However, ensuring that the implemented code aligns with these criteria is crucial for delivering high-quality software. This talk explores the integration of generative AI generating acceptance criteria with test case scenarios, aiming to establish a seamless connection between the development and testing phases. By leveraging pull requests as a central hub, this approach facilitates the validation of whether the test case scenarios adequately cover the...
“Low Code”—Coded Automation Using Free Tools
Using artificial intelligence to generate test code is a hybrid automation strategy that combines the best of both worlds. Tests can be created very quickly by almost anyone using AI, yet the tests are still planned by humans and maintainable by humans. With the right prompts, you can have AI construct traditional test code using open source testing tools that the world is already familiar with (Chai, Mocha, Cypress). As a result, you end up with structured code that is logical and easy to maintain without having to wonder what the AI is testing. In this session, Timothy will look at...
The Quality Assurance of Artificial Intelligence: How to Test the Tests!
Many companies are asking the question: "What can AI do to improve our QA practices?" But is this the right question? David believes that as QA practitioners the real question is "How should we be preparing to efficiently test emerging and complex ML an AI systems?" AI testing is different from traditional testing because AI systems are not static, but dynamic and adaptive. They learn from data and feedback, and change their behavior accordingly. This means that AI testing must account for the variability and unpredictability of AI systems, and ensure that they are not only functional, but...
Multi-Modal GPTs Are Coming For Your Testing! How to Adapt?
As you research the latest in generative AI technology, you will see that the development and availability of multi-modal GPT engines are fundamentally changing the way applications are tested and described. These new GPT models can generate and interpret voice, text, and images seamlessly. For example, you can ask them to navigate an application to accomplish a business task and comment on their actions. This means that we’re for the first time entering the world of AI-assisted/performed exploratory testing. When you couple this with the capabilities of GPT models to identify UI elements...