SmartBear announced AI enhancements for API testing, UI test automation, and test management across its product suite, the SmartBear Application Integrity Core™.
The application of maturity metrics to everything that we do in today's business environment frequently creates the requirement to perform difficult, far-reaching calculations.
It's not necessarily those measurements that span huge sets of complex data that present the most challenging prospects. Often, it's a compilation of those metrics that attempt to analyze the advancement of fuzzier, process-oriented initiatives that can leave one grasping for just the right analysis methods.
Attempting to weigh the current level of DevOps maturity within your organization is precisely one of those daunting propositions that can leave today's business and technology pros searching for meaningful answers.
Sure, there are some well-established metrics that can serve as inherent measurements of overall DevOps success, including deployment frequency rates, average lead times, meant time to recovery (MTTR), and of course, any figures resulting from dedicated Application Performance Monitoring (APM).
Yet, perhaps even more valuable than some of these numbers, or of greater import to practitioners for purposes of self-assessment, are metrics that help analyze precisely how ongoing DevOps adoption compares to similar efforts among peers.
At the end of the day, widely touted unicorns can publicize stunning evidence of their agile transformations, driven by DevOps methodologies; yet, for most organizations this is a long-term, iterative process aided greatly by some understanding of how they compare to less revolutionary examples.
After all, getting a feel for where you're ahead of the curve or behind the 8-ball might be just the thing to help DevOps-oriented teams offer evidence of progress, or the need for increased investment, the next time management comes looking for answers.
For instance, related to development, perhaps your teams are already actively tracking feature request lead times; but is there an agreement between business, dev and ops regarding the performance of critical services (transaction counts, performance, uptime, etc.) necessary to meet pre-defined business goals?
In the deployment arena, you likely have systems in place to note changes in frequency; however, does your organizational structure and tooling support cross-functional teams that put greater emphasis on the processes associated with releasing new capabilities, rather than supporting individual roles?
As far as management is concerned, you're probably employing APM to ensure improved visibility, response, uptime and availability. That said, is your monitoring able to distinguish the most critical and recurrent problems, and how they impact business services – without necessitating lengthy configuration and base-lining?
Industry News
JFrog announced its partnership with iZeno Pte Ltd, a Singapore-headquartered enterprise technology solutions provider.
Red Hat announced an expanded collaboration with Google Cloud to help organizations accelerate application modernization and cloud migrations.
The Linux Foundation, the nonprofit organization enabling mass innovation through open source, announced the contribution of SQLMesh, an open source data transformation framework, to the Foundation by Fivetran.
Check Point® Software Technologies Ltd. released the AI Factory Security Architecture Blueprint — a comprehensive, vendor-tested reference architecture for securing private AI infrastructure from the hardware layer to the application layer.
CMD+CTRL Security won the following awards from Cyber Defense Magazine (CDM), the industry’s leading electronic information security magazine: Most Innovative Cybersecurity Training and Pioneering Secure Coding: Developer Upskilling.
Check Point® Software Technologies Ltd. announced the Check Point AI Defense Plane, a unified AI security control plane designed to help enterprises govern how AI is connected, deployed, and operated across the business.
Oracle announced the latest updates to Oracle AI Agent Studio for Fusion Applications, a complete development platform for building, connecting, and running AI automation and agentic applications.
The Cloud Native Computing Foundation® (CNCF®), which builds sustainable ecosystems for cloud native software, announced that Istio has launched a host of new features designed to meet the rising needs of modern, AI-driven infrastructure while reducing operational complexity.
Chainguard announced Chainguard Repository, a single Chainguard-managed experience for pulling secure-by-default open source containers, dependencies, OS packages, virtual machine images, CI/CD workflows, and agent skills that have built-in, intelligent policies to enforce enterprise security standards.
Backslash Security announced new cross-product support for agentic AI Skills within its platform, enabling organizations to discover, assess, and apply security guardrails to Skills used across AI-native software development environments.
The Cloud Native Computing Foundation® (CNCF®), which builds sustainable ecosystems for cloud native software, announced the graduation of Kyverno, a Kubernetes-native policy engine that enables organizations to define, manage and enforce policy-as-code across cloud native environments.
Zero Networks announced the Kubernetes Access Matrix, a real time visual map that exposes every allowed and denied rule inside Kubernetes clusters.
Apiiro announced AI Threat Modeling, a new capability within Apiiro Guardian Agent that automatically generates architecture-aware threat models to identify security and compliance risks before code exists.
GitLab released GitLab 18.10, making it easier and more affordable to use agentic AI capabilities across the entire software development lifecycle.




