Software Testing Articles & Tutorials: Load Testing, Unit Testing, Functional Testing, Performance Testing, Agile Testing, DevOps
There is a particular kind of risk that emerges when a SaaS environment becomes familiar. The dashboards look healthy. Authentication works. Backups run. Nothing is triggering an alert, and nobody has noticed anything suspicious. But familiarity has its own weakness; people stop looking closely at things they believe they understand.
Today, you can find tools that generate code and AI-assisted test automation tools that will handle the quality assurance part. Is it the death of traditional manual testing? In this article, Rimpal Mistry reminds us that human validation is still needed to provide the final check of delivered software.
In software development, risk isn’t just for project managers. It’s a daily reality for quality assurance (QA) and compliance teams too. While one team looks for bugs and performance issues, the other makes sure the product follows legal and industry rules. Often, these two groups work separately, only connecting when a problem forces them to.
A customer’s first interaction with your software shapes their entire relationship with you. A smooth, intuitive onboarding process can create a loyal advocate, but a clunky, confusing one can lead to immediate churn. For quality engineering teams, making sure the onboarding experience is frictionless isn’t just about finding bugs anymore.
AI insurance verification uses artificial intelligence and workflow automation to help healthcare organizations confirm patient coverage, benefits, and authorization requirements.
Did you know that 92% of customers check online reviews before purchasing anything? This means that app reviews are much more than just a measure of customer satisfaction. These reviews can also show errors and usability issues that might not be captured by your QA team.
Like code, you can ask AI to generate your unit tests. But what about the quality of the resulting scripts. Who is going to check the coverage of the resulting to these automated tests? This article discusses the issue of ensuring that every AI-generated unit test contributes unique value is becoming the real challenge.