AI testing

The rise of artificial intelligence has redefined the boundaries of software testing. Whether you are testing AI models for bias and accuracy or using generative AI to automate your test suites, staying updated is crucial. Here, we explore the intersection of AI and QA, covering everything from MLOps validation to advanced AI-powered testing tools that help teams achieve unprecedented scale and efficiency in 2026.

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AI AI testing automation testing test management

Test Case Generation Using LLMs: How AI Is Transforming Software Testing

According to different estimates, the global number of software testers doesn’t exceed 9 million. Given the meteoric spike in the production of all kinds of IT products and the respective quantity of software tests to be conducted, QA teams can’t get them covered, no matter how hard they try. The only way to ensure a […]

Vitaliy Mikhailyuk

Dec 30, 25

AI testing MCP test management

Python MCP Server: Building AI Context with Examples

The current practice of using external data and tools for AI model testing and development brings substantial benefits, but the need for custom system connections leads to major problems with system expansion capabilities. Thanks to the Model Context Protocol (MCP), you can establish a standardized client-server framework to enable LLMs to connect with external systems […]

Michael Bodnarchuk

Dec 26, 25

AI AI testing automation testing Insights

ChatGPT for Test Case Generation: How to Use AI Prompts for Testing

Let’s be real, software testing can be exhausting. You spend hours analyzing requirements, brainstorming test scenarios, writing detailed test cases, and then finally getting to the fun part: actually running the tests. But here’s the thing: while clicking buttons is easy, everything that comes before it? That’s where the real time-sink happens.

Vitaliy Mikhailyuk

Dec 22, 25

AI AI testing automation testing Insights MCP

Best Tools to Test MCP Server in 2026: A Complete Guide

The Model Context Protocol (MCP) represents a fundamental shift in how AI agents interact with external tools and data sources. Developed by Anthropic and quickly becoming an industry standard, MCP creates a universal way for large language models to securely access information and execute actions across different systems.

Michael Bodnarchuk

Nov 18, 25

AI AI testing testing tool

AI-Driven Test Design: Transform Requirements into Intelligent Test Cases

As the need for digital solutions in the contemporary IT-fueled world grows exponentially, development teams find it increasingly hard to churn out websites, apps, e-commerce platforms, ERP systems, etc., in sufficient numbers. And the non-negotiable requirement for all these products is unimpeachable software quality, validated through out-and-out software product testing.

Michael Bodnarchuk

Nov 14, 25

AI testing jira MCP test management

Building Jira MCP Server integration with test management

With the Model Context Protocol (MCP) you can communicate with Attlasian’s Jira or Confluence tool through natural language interactions. It delivers a strong ability to eliminate the need to leave the current AI application to complete Jira-related tasks

Olga Sheremeta

Nov 08, 25

AI AI testing testing tool

AI-Powered Monitoring for Automation Testing: The 2026 Guide

The process of creating and delivering software is speeding up, and automation testing has become one of the critical aspects of continuous integration and deployment (CI/CD). Automated tests are able to process thousands of cases in minutes and generate lots of valuable data.

Michael Bodnarchuk

Oct 24, 25

AI testing MCP playwright

Playwright MCP Claude Code: Complete Guide to AI Test Automation

The test automation space is turning into a huge coding AI partnership for development and testing teams and AI. Claude Code, which is combined with Playwright Model Context Protocol (MCP), transforms the way developers and QAs approach browser automation and testing. With the MCP Playwright server,

Olga Sheremeta

Oct 15, 25