The rapid growth in software delivery as a solution for all business needs for organizations has intensified the demand for efficient and comprehensive software testing processes. Ensuring the quality, reliability, and security of software products is of paramount importance as they become increasingly integrated into various aspects of our lives.
Katalon and OctoPerf play a critical role in the continuous performance testing process, where OctoPerf helps to build the performance activity, and Katalon triggers the build activity and pushes performance reports into the Katalon platform. By integrating Katalon into OctoPerf, you can ensure that you have comprehensive end-to-end test coverage in a fully automated way. The integration between these two solutions will help you automate the build, test, and deployment of your applications.
In software performance testing the landscape is continuously evolving, driven by advancements in technology and the growing complexity of modern applications.
This years I/O can be easily summed up by just one phrase: “AI”. AI absolutely everywhere. Sundar Pichai started, mentioned numerous times and ended the Keynote by mentioning AI. Some of the main announcements around AI for developers included the general availablity of “Bard” supporting 20 programming languages and use of the “PaLM 2” based models with some like “Gecko” small enough to run on mobile devices.
TL;DR: M2 machines are up to 33% faster than M1 machines! For macOS and iOS builds, we see build time improvements of ~50% compared with the Mac Pro.
Hitachi Vantara has once again been recognized as a leader and fast mover in the 2023 GigaOm Radar for Unstructured Data Management: Infrastructure-Focused Solutions, marking the third consecutive year we have achieved this honor. The report emphasizes the growing complexity of unstructured data management and highlights the importance of having a solution that can seamlessly handle data movement at scale automatically.
In the first three articles in this four-post series, my colleague Jason English and I explored DataOps observability, the connection between DevOps and DataOps, and data-centric FinOps best practices. In this concluding article in the series, I’ll explore DataOps resiliency – not simply how to prevent data-related problems, but also how to recover from them quickly, ideally without impacting the business and its customers.
The GPT euphoria got doused with some reality recently as Samsung employees realized they were sending false information to customers and Italy outright banned ChatGPT. The hype and concerns further accelerated last week with the godfather of AI, Hinton, resigning from Google, President Biden summoning AI leaders to Washington, and several stocks nose-diving on the threats generative AI poses to their business models.
Developer productivity is a complex subject for which there is no magic bullet. However, economic pressure, increased market competition and shorter delivery circles force many organisations to improve their efficiency and to open up new models of operations. Measuring, maintaining and eventually improving engineering productivity in an increasingly hybrid workplace are important discussions many organisations are having right now.
