Apply AI To Keep Track and Manage Failed Test Cases
Manage failed test cases is no longer just about recording what went wrong. In modern QA workflows, teams need to understand which failures matter, which ones keep coming back, and what should be prioritized next. However, many teams still face...
How To Create Test Cases Effectively With AgileTest Generator
Create test cases with AgileTest Generator can help address this challenge. While many testing tools can automate test case generation, they often depend on well-prepared requirement descriptions. This article explores the limitations of current AI tools for test case generation...
Jira Testing Plugin: Top 5 With Advanced AI Features in 2026
Jira Testing Plugin has revolutionized software testing, offering tools that integrate directly into your Jira environment. With the increasing demand for efficiency and quality in testing, Jira testing plugin now leverage advanced AI features to automate and enhance key testing...
AI for Regression Testing: Challenges, Benefits, and What’s Next
AI for regression testing is becoming increasingly relevant since software teams release changes faster and more frequently. Traditional regression testing plays a critical role in protecting existing functionality, but it often struggles. This article briefly walks through the regression testing,...
AgileTest Summary & Analyzer: A QA/QC Assistant That Consolidates Your Test Data
AgileTest Summary & Analyzer is an AI assistant built to solve a problem every QA and QC team eventually faces, not a lack of data, but the time it takes to turn that data into clear answers. In a typical...
AgileTest Generator: Preparing Test Cases Through AI-Guided Conversations
AgileTest Generator is built to change how teams prepare test cases by turning conversations with AI into executable test cases. Testers can collaborate with the AI assistant that understands context, asks follow-up questions, and guides test design step by step....
Data Granularity: The Hidden Factor Behind AI Testing Quality
Data granularity plays a crucial role in how we understand, evaluate, and improve AI systems. In AI testing, granularity isn’t just a data feature but directly impacts how accurately we measure model performance. In this article, we’ll explore why data...
Self-Corrective RAG: How It Works, Why It Matters, and How to Implement It
Self-corrective RAG is quickly becoming one of the most important techniques in AI testing. While traditional RAG helps models pull information from external documents, it still struggles with issues. This article breaks down what RAG is, its limitations, and how...
AI Testing Evaluators for Scalable, Reliable QA
AI Testing Evaluators are becoming an essential part of modern software AI Testing processes. While AI can produce output at impressive speed, ensuring that this output is accurate, complete, and aligned with real product behavior is a new challenge for...
How to Test Smarter with the New AgileTest Agents
We’re excited to introduce Rovo Agent, the latest innovation: AgileTest Agents that bring the power of Atlassian’s AI technology directly into your testing workflow. With this release, testers and QA teams can now manage executions, analyze test progress, and generate...