Is there a formula or approach to estimate how much you should spend on your quality engineering (QE) efforts? Our CEO, Steve Semelsberger, is frequently asked that question in discussions…
Is there a formula or approach to estimate how much you should spend on your quality engineering (QE) efforts? Our CEO, Steve Semelsberger, is frequently asked that question in discussions…
What can quality assurance (QA) teams expect in 2025? This article will cover what software testing trends QA teams should prepare for to succeed this year.
The deadline is looming, June 28th, 2025, and a lot of companies are keen to tell you how they can help you navigate the new European Union member states’ enforcement of the EAA.
Did you know that 90% of mobile users abandon an app because of bugs or performance issues? Smooth app functionality is key nowadays. Without it, users leave quickly.
Large language models are under threat from a tactic called LLM grooming, where bad actors flood public data sources with biased or misleading content to influence AI training behind the scenes.
Smartphone users worldwide are expected to reach 7.7 billion by 2028. With such an enormous and growing user base, non-functional requirements like performance, security, reliability, and usability can make or break businesses.
In quality engineering, axioms are foundational truths drawn from years of practice that underpin effective, scalable automation systems.
A quick internet search provides examples of marketing, brand, and UX issues when localization testing is treated as an afterthought.
Many software applications struggle to handle high user loads, leading to slow performance, crashes, and a poor user experience.
QA Automation empowers teams to deliver higher-quality software faster by reducing manual effort, minimizing errors, and accelerating the entire testing process.
Test case prioritization helps you rank test cases based on importance. It ensures you test the most critical features first. This approach saves time, improves software quality, and helps you catch high-risk defects early.
Finding a bug is one thing, but documenting it is just as important, if not more so.
AI systems are only as reliable as the testing behind them. Red teaming brings a fresh, proactive approach to testing by helping you spot risks early.