STARWEST 2024 - Agile Testing

To be most effective, leaders—including development and testing managers, ScrumMasters, product owners, and IT managers—need metrics to help direct their efforts and make informed recommendations about the software’s release readiness and associated risks. Because one important evaluation activity is to “measure” the quality of the software, the progress and results of both development and testing must be measured. Collecting, analyzing, and using metrics are complicated because developers and testers often are concerned that the metrics will be used against them. Join Mike Sowers as he...

When it comes to software quality, data and metrics are essential for making informed decisions. However, not all KPI metrics are created equal. In fact, many common KPI metrics can become myths that do more harm than good. In this tutorial, Lisette draws on her experience to debunk the QA KPI myths and share practical strategies for making KPI metrics great again. She will discuss how to determine the purpose of QA KPIs for your organization's needs and how to adapt KPI metrics to your organization's culture—exploring practical QA KPI metrics that bring value to everyone in the...

Avoiding siloed development is a tricky business. It’s so easy for agile teams to fall into the rut of testers only doing traditional testing activities and programmers strictly doing their time-worn coding activities. But there are actually many places testers can be influential and contribute value on cross-functional teams. Robert Sabourin will share several examples of how testing skills can be applied to a wide variety of activities in an agile project. For instance, testers are among the most skilled team members in story grooming, elicitation, and exploration. Risk analysis in self-...

It is known that developers are most productive when they can stay in flow, but also that delaying or omitting tests can cause disruption, breakage, and security issues down the line. By properly leveraging background automation, team collaboration, and machine learning in your native DevOps workflow, you can get the best of both worlds: productive flowstate AND safe code. In this talk, Jon Peck will take a look at ways to take advantage of asynchronous test notifications, single-pane-of-glass reviews, and GPT-based tools for faster coding, test generation, and vulnerability remediation....

In today's data-driven landscape, real-time data processing is the backbone of modern applications. Kafka, a distributed event streaming platform, has emerged as a critical component in building data-intensive systems. This presentation provides an in-depth exploration of Kafka and the essential practice of Kafka testing, offering attendees valuable insights into harnessing the power of real-time data streams while ensuring robust system reliability. Key topics to be covered are: an introduction to Kafka to understand Kafka's architecture and core components, importance of Kafka Testing,...

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...

It has been a commonly held belief that an ever growing list of manual test cases, kept and maintained by the QA team is the best way to provide training, metrics and a roadmap for future automation efforts. But what if this base assumption is wrong? What if instead of having just a number of passing or failing test cases, we could better speak to factors like risk and quality before our products go live and have a little extra time to be creative with our testing? Rebecca Peterson wants to teach you the steps she and her coworkers have made to move towards living documentation that can...

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...

As the lone tester amongst a sea of developers, Sylvia Solorzano struggled to scale reactive efforts into a cohesive program. Haphazard bug reports strained relationships and failed to provide direction. By installing a phased methodology based on industry best practices first, quality became her north star. Sylvia enshrined requirements traceability and manually executed test cases for each function delivered. Then she evaluated integrations between components and expanding to validate full system behavior as users would experience. Finally, leveraging test standards, she layered on...

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...