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The latest News and Information on Software Testing and related technologies.

Code coverage vs. test coverage in Python

If you have been writing tests for a while, you have probably encountered code coverage and test coverage. These concepts can be difficult to differentiate because they are somewhat intertwined. In this article, you will learn what code coverage vs test coverage means, and the basis of these concepts. You will also learn the key differences between code coverage and test coverage in Python. You would discover tools, techniques, and best practices to improve your testing strategy.

Top 5 BrowserStack Alternatives in 2026

BrowserStack is a popular web and mobile testing platform, but in 2026 many teams are actively searching for BrowserStack alternatives to simplify testing, lower costs, and automate at scale more efficiently. But how do you pick the right BrowserStack alternative testing tool? Do you rely on user reviews, popularity, or the most budget-friendly option?

Software Quality Gates: How Do They Work?

Shipping fast feels great – until something breaks in production. Sometimes, even solid-looking builds fail just because one small issue slipped through testing. That’s where software quality gates step in. They act as automated checks that stop risky code before it moves ahead in the pipeline. Rather than relying upon instinct, we rely on data – code coverage numbers, test results, and security signals.

What Is MCP? Connecting AI Across the Software Delivery Lifecycle

AI promises speed and automation — but most teams are still stuck jumping between disconnected tools across development, testing, and operations. In this video, we introduce the Model Context Protocol (MCP) and how it enables AI assistants to securely access tools, systems, and real-time context across the software delivery lifecycle. MCP is the foundation of Perforce Intelligence, allowing AI to: The result: less friction, faster feedback, and AI that works with your existing systems — not around them.

Powering agentic software quality with MCP servers | From the Bear Cave

In this From the Bear Cave session, Dan Faulkner, CEO of SmartBear, and Vineeta Puranik, CTO/CPO of SmartBear, discuss why agentic automation is becoming essential in software development and delivery, how MCP Server enables connected autonomy across the SDLC, and what this transformation means for business outcomes and the future of human + AI collaboration.

AI in QA: What leading quality experts want every team to know

Our goal with the Tricentis blog is to distill insights that help QA professionals navigate the massive, AI-driven transformation happening across the software delivery landscape. To that end, I reached out to experts across Tricentis, from product and services to marketing and strategy, to hear what they’re really thinking about AI in QA right now. This group brings decades of experience building testing products, guiding enterprise transformations, and shaping how organizations adopt AI.

What challenges was the customer facing before Katalon, and how were they testing then?

They struggled with constant production bugs, poor quality, and low automation, so much so that the VP of Engineering couldn’t unplug on weekends for fear the website might break. With only ~20% automation and frequent releases across web, mobile, and APIs, they lacked the coverage and resilience needed to prevent defects from leaking into production. — Daisy Hoang, SVP Revenue at Katalon Follow Katalon for more insights in our series!

What is an MCP? Breaking Down the Model Context Protocol

70% of teams are already integrating generative AI tools into their daily workflows, according to our 2025 State of Game Technology Report. Now more than ever, teams are looking to connect their AI tools to the services and applications they rely on to get work done. To address this issue, the industry has begun to standardize using the Model Context Protocol (MCP) to connect their existing tools and LLMs like Claude, GPT, and Gemini.

Best Practices for AI in CI/CD QA Pipelines

AI transforms CI/CD testing from reactive bug detection into proactive quality assurance that accelerates release cycles while improving software reliability. Start embedding AI into your testing workflows now because teams that wait will struggle to match the velocity of competitors who already have. Continuous integration and continuous deployment pipelines have become the backbone of modern software delivery.