The 2024 DORA Accelerate State of DevOps Report provides a warning: AI use was associated with a 7% decrease in stability ...

A DevOps-based framework integrates automation, continuous integration, and agile methodologies to enhance medical device software development. By embedding compliance verification, automated testing, ...

Overview DevOps automation tools help startups streamline deployment, testing, and monitoring workflows.Tools offering scalability, cloud integration, and CI/CD ...

Explore the best security risk assessment tools of 2025 to address modern cybersecurity challenges, enhance risk management, ...

Discover the top 7 Dynamic Application Security Testing (DAST) tools for enterprises in 2025. This guide provides insights into features, pricing, and selection criteria to help ensure effective ...

Nearly a month after the debut of the AI-laden Visual Studio 2026 Insiders edition, Microsoft dropped a new build, citing performance improvements, especially for startup and hot reload. However, you ...

As DevOps practices mature and Continuous Integration/Continuous Deployment (CI/CD) pipelines become more deeply embedded in the software delivery lifecycle, the ...

Google is bringing its AI coding agent Jules deeper into developer workflows with a new command-line interface and public API, allowing it to plug into terminals, CI/CD systems, and tools like Slack — ...

Do Your CI/CD Pipelines Need Identities? Yes. Originally published by Aembit. Written by Apurva Davé. If one principal can do anything, one mistake can undo everything. I’ve read too many incident ...

What if the biggest bottleneck in your software development process wasn’t your team’s skillset or tools, but the lack of a clear, structured roadmap? For years, developers have wrestled with the ...

Abstract: Manual security policy validation of Infrastructure-as-Code (IaC) creates bottlenecks in enterprise CI/CD pipelines, with 90% of cloud breaches involving misconfigured IaC. Traditional ...

ABSTRACT: Introduction: DevOps maturity models help organizations benchmark their engineering capabilities, yet the empirical grounding of most models remains fragmented in scholarly literature.