Article four in the “Quality Engineering for CEOs” series brings you an exploration of the role of software quality in organizational confidence.
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.
In quality engineering, axioms are foundational truths drawn from years of practice that underpin effective, scalable automation systems.
One of the biggest challenges in software testing is not having the right visibility. Too little knowledge, and you’re guessing. Too much, and you get bogged down in code.
Quality engineering and assurance efforts, and testing as a part of those, have never been more critical than they are today.
Software systems are becoming more complex and interconnected every day, and as a result, effective testing is more important than ever.
What if the first bug your users find shakes their trust? In a fast-moving release cycle, you only get one chance to make a solid impression, and poor quality can cost more than just rework.
Imagine a food delivery application with a feature for scheduling orders, but this functionality fails during peak user traffic. Executing performance testing with simulated peak traffic can prevent such failures and improve the user experience.
Volume testing is a type of software performance testing that evaluates a system’s capacity to process massive data volumes within a specific timeframe. It identifies bottlenecks, crashes, or inefficiencies under high data loads, ensuring performance, accuracy, and stability.
As software systems are updated and new bugs are created, previously functioning features may stop working as intended.