testRigor Blog

Weekly QA Testing Knowledge

Top Challenges in AI-Driven Quality Assurance

Artificial Intelligence (AI) enables higher efficiency, accuracy, and scalability in software testing than traditional approaches. AI-based QA uses sophisticated algorithms to facilitate test case generation, defects detection, and predictive analysis with less human intervention but faster. However, using AI within QA workflows is not just sunlight and rainbows. There are inherent complexities in implementing it …

Test Automation Maturity Model

In today’s competitive market, organizations must deliver high-quality software products at lightning speed. Test ...

STLC vs. SDLC in Software Testing

Imagine a developer building a new e-commerce website. They follow the Software Development Life Cycle or SDLC to design the user ...

Requirement Traceability Matrix RTM: Is it Still Relevant?

The Requirement Traceability Matrix or RTM has traditionally done the heavy lifting in software development and QA as a ...

Paid Automation Testing Tools: Not All Created Equal

As software testing trends continue to progress, codeless automation tools have gained traction among teams looking for faster ...

Technology Risk Management: A Leader’s Guide

Technology has become central to almost every operation within the modern business. Today, tech companies allow dynamic options ...
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