Systems | Development | Analytics | API | Testing

The next evolution in QA: How AI is changing software testing

Shipping high-quality software quickly is challenging. QA professionals are facing pressure to test more, faster in a world where GenAI is pushing delivery – all while trying to cut costs. For years, manual testing and traditional automation tools like Selenium have been the standard. But both come with challenges. Manual testing alone can be slow and prone to errors, while Selenium and similar tools require coding expertise, need constant script maintenance, and are easily broken by UI changes.

How to create a test cycle | Zephyr

SmartBear Zephyr is the Jira-native test management and automation platform that empowers your team to deliver better software, faster. Organizing your testing into Test Cycles is the best way to align your QA efforts with specific sprints, releases, and versions, ensuring your team stays focused and on schedule. This short demo video guides you through the process of setting up and populating your Test Cycles. You’ll learn how to define execution windows, link cycles to specific release versions, and pull in test cases from across different Jira projects to create a unified testing effort.

Tracking testing progress with reports | Zephyr

SmartBear Zephyr is the Jira-native test management and automation platform that empowers your team to deliver better software, faster. Its reporting and dashboard capabilities provide real-time visibility into your quality metrics, so you always know the status of your release. This short demo video shows you how to navigate the Zephyr Reports tab and build customized Jira Dashboards. Whether it’s via high-level execution summaries or deep-dive traceability reports, you can track coverage and identify testing bottlenecks instantly.

Introducing Agent-Flavored Markdown (AFM): No Code, Portable AI Agents

Advances in large language models (LLMs) and their widespread accessibility have transformed both what software can do and how we build it. The use of LLMs has quickly evolved from simple single-turn interactions to AI agents that reason, use tools, manage state, and operate autonomously.

15 Best Automated API Testing Tools (Compared for 2026)

The number of API testing tools on the market for you and your team to try out and select will probably take forever. Here are several key considerations to keep in mind when choosing one: Smart Summary Our comprehensive analysis of automated API testing tools provides crucial insights for teams aiming to optimize their development workflows.

Software Testing Strategies: A Complete Practical Guide (2026)

Software testing strategies define how I plan, structure, and execute quality checks across the entire software development lifecycle to maintain reliable software outcomes. With teams shipping faster through Agile, DevOps, APIs, and CI/CD pipelines, relying only on ad-hoc or manual workflows no longer works. I’ve seen structured strategies supported by the right tools become essential for controlling risk while still moving fast. Let’s explore how this works in real software teams.

My AI Agent Stole My Crypto #speedscale #openclaw #aicoding #codingagent #security

I thought I found the ultimate coding shortcut: an autonomous AI agent. Turns out, I just bought a one-way ticket to a digital nightmare. A friendly reminder to my fellow devs: Validation isn't optional—it's survival. Your laptop shouldn't have a higher calling than your production environment. Validate now: speedscale.com.

How to Connect LLM Chat and AI Agents to Enterprise Data Using Built-In MCP in DreamFactory

TL;DR: DreamFactory 7.4+ includes a built-in MCP (Model Context Protocol) server that lets you connect any LLM—ChatGPT, Claude, Perplexity, or custom AI agents—to your enterprise databases through governed, role-based APIs. Setup takes minutes: create an MCP service in the admin console, copy the OAuth credentials, and point your AI application to the generated endpoint.