Systems | Development | Analytics | API | Testing

Zephyr walkthrough: Test management & automation for Jira teams

Discover how Zephyr transforms test management in Jira with this comprehensive walkthrough covering test automation, CI/CD integration, and scaling QA workflows. Struggling with fragmented testing tools, manual processes, and limited visibility into your QA pipeline? This complete Zephyr tutorial shows developers and QA teams how to build a unified, high-performance test management system directly in Jira – without compromising speed or collaboration.

How regulatory organizations can modernize API testing without compromising compliance

Picture this scenario: Your organization is three days away from a critical compliance audit. The auditors have requested comprehensive documentation of your API testing processes, including security testing results, change management logs, and validation records. As you and your QA team scramble to compile reports from multiple tools and spreadsheets, a sinking realization sets in.

Swagger in 2025: Accelerating the Journey to AI-Ready API Quality

2025 underscored a simple reality: APIs are now expected to serve both human developers and intelligent systems, and the tools supporting those APIs must evolve just as quickly. Major cloud providers (OpenAI, Google Cloud, Azure, AWS, Hugging Face, Cohere, etc.) now earn significant revenue by exposing their capabilities via APIs, which are then chained by other AI systems to build chatbots, copilots, and autonomous agents.

2025 for ReadyAPI: A Look Back to the Year of Scale and Innovation

As we close the books on 2025, for many organizations, APIs became more than technical plumbing, they evolved into strategic assets that determine competitive advantage, customer experience, and operational resilience. ReadyAPI’s evolution in 2025 wasn’t just about adding features – it was about fundamentally transforming how enterprise teams approach API quality, speed, and scale.

Continuous Quality Signals: Connecting Jira, Zephyr and BugSnag for Risk-Based Testing

Engineering teams want to understand the real health of their applications – not just what was planned or what was tested, but what is actually happening in production. The challenge is that these signals live in different systems, each optimized for a specific part of the delivery lifecycle. Test execution data, issue tracking, and production monitoring each describe a different aspect of system behavior. On their own, they answer narrow questions about validation, delivery, or stability.

Accelerating the API SDLC with SmartBear MCP Server and Swagger MCP Tools

Note: The SmartBear MCP Server is under active development and features may change. Check our GitHub repository for the latest updates and compatibility information. At SmartBear, we show how our MCP Server enables a secure and intelligent bridge between SmartBear platform data and AI-powered development workflows.

Reaching Autonomous Software Quality | From the Bear Cave

AI has changed how software is written, but testing and quality haven’t kept up at the same pace. In the first episode of our "From the Bear Cave" discussion series, SmartBear CEO Dan Faulkner and VP of Product, AI, Bridges Smith talk through the Levels of Autonomy in software development, why non-code-based testing must become more autonomous, and what it will take to close the gap between AI coding and ensuring trustworthy software.

Keep Pact Tests Up to Date Automatically | SmartBear MCP Server

Tired of manually updating your Pact contract tests? Learn how to automate the entire process using AI agents with SmartBear MCP tools and GitHub Copilot! In this video, we walk through how AI-assisted contract testing helps you automatically upgrade Pact tests when new features are added to your API.

Automate Pact Test Code Reviews with AI | SmartBear MCP Server

Discover how to perform expert-level Pact contract testing reviews in seconds using SmartBear MCP (Model Context Protocol) tools directly in your IDE with GitHub Copilot (or any generative AI that supports MCP)! You’ll learn how to automatically identify and fix common issues in your Pact tests, including: Over-specified test contracts Incorrect HTTP client usage Missing provider states Overuse of matchers Tests not reflecting real API behavior.

Generate Full Pact Tests Automatically | SmartBear MCP Server

Did you know you can automatically generate complete Pact test suites directly inside your IDE? No more writing boilerplate test code manually! In this video, we show how SmartBear MCP tools work with GitHub Copilot (or any MCP-compatible agent) to automatically create best-practice consumer contract tests tailored to your project.