STAREAST 2023 - Big Data, Analytics, AI/Machine Learning for Testing
Tuesday, May 2
A Quality Engineering Introduction to AI and Machine Learning
NewAlthough there are several controversies and misunderstandings surrounding AI and machine learning, one thing is apparent — people have quality concerns about the safety, reliability, and trustworthiness of these types of systems. Not only are ML-based systems shrouded in mystery due to their largely black-box nature, they also tend to be unpredictable since they can adapt and learn new things at runtime. Validating ML systems is challenging and requires a cross-section of knowledge, skills, and experience from areas such as mathematics, data science, software engineering, cyber-security,...
Wednesday, May 3
RIP UI Test Automation. You Failed. CX Observability Will Lead Us to Software Quality
Despite the massive efforts around testing phases, pyramids, vegetables, periodic table, etc., users keep finding bugs, which leads to poor CX. Testing is an activity; quality is the goal. We’ve been focused on making the activity better, but that didn’t improve quality. In this session, Alex Martins will discuss his best efforts and experiences around testing and never being able to achieve the dream of software quality. Alex has been on projects that spent more money on testing than on development, and still didn’t catch all bugs. He's thrown AI/ML into my testing lifecycle and still...
Leveraging Machine Learning & AI for Quality Assurance
Machine learning, artificial intelligence… It’s everywhere. Everyone is talking about it. It’s the next “BIG” thing that is expected to bring in over 10 trillion into the economy by 2030. But what is it? And what can it do for you? Join David for an explanation on machine learning and AI and the many benefits each provides. Furthermore, you will dive deeper into three areas in which machine learning and AI can be utilized to improve the quality of your software and users’ experience, such as: 1. Utilizing machine learning to identify patterns in analytics to essentially provide enhanced...
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Data Science and System Testing: Lessons Learned from a Four-Part Workshop Series
During fall 2022, Big Data Florida ran a series of four workshops on the intersection of data science and system testing. The first was on “Testing Big Data Systems,” which provided the basis for subsequent meetings. The second event was on “Testing Machine Learning (ML) Models,” the third was on “Testing Artificial Intelligence (AI) Applications,” and the fourth was on “Leveraging AI and ML in Testing.” Participants were an interdisciplinary group drawn from data science, testing (hardware, software, and integrated systems), and interested researchers and professionals. This session...
Thursday, May 4
Automatic Canary Analysis: Critical Success Factor for Release Pipeline
Netflix began streaming services with a small-scale microservice ecosystem. As our global reach grew, so did the complexity of the underlying ecosystem. Membership Lifecycle Ecosystem is a cluster of services that handles all the backend business logic related to membership, signup, billing, and payments. This ecosystem consists of 100+ internal microservices and a similar number of external partners and payment processors - spanning across multiple teams. Each team has an asynchronous release cadence and the quality of each release has a direct impact on the quality of the ecosystem and...
Ukrainian Lessons Learned: How to Build Continuous Testing and Project Management from the Trenches
PreviewOn the 24th of February, life in Ukraine changed dramatically, and it hasn't stopped. Projects had to be delivered according to release schedules and Business Continuity Plans (BCP) had to be built in parallel with deploying shelters and evacuating teams to safe zones. Despite risk mitigation plans, the war was such a critical factor that the existing hierarchal management structure began to show inefficiency. As two Directors of Software Quality in a company with 14,000 employees in Ukraine, Maryna will share their approach to governance of software quality, release management, and...