STARWEST 2024 - Test Transformation
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...
Testing for Synergy: Progressive Testing Strategies for Interdependent Product Suites
In today's dynamic business landscape, organizations grapple with the challenge of innovating across multiple product developments simultaneously. This session explores the intricacies of concurrent development involving seven products, highlighting the pivotal roles of scrum teams, engineering approach, and collaborative testing strategies. Traditional independent product testing approaches prove insufficient in the context of interdependence and a unified platform. The discussion centers on the need for an evolved testing strategy that shifts left, meticulously addressing integration...
Traffic-Driven Testing—A Primer on Kubernetes Performance Engineering
Migrating your apps to Kubernetes and breaking up monoliths pose a unique set of testing challenges. Container-based testing gives us enormous flexibility and scalability and your testing strategy should take that into account. Waiting for a full environment and simply migrating your test scripts is risky and doesn't take advantage of Kubernetes container management features. Making sure your migrated app can handle normal load as well as spikes requires careful planning. You may also want to compare different clouds. In this talk, Matthew will go through some proven strategies for using...
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...