Testlio blog

We are thrilled to share that Testlio has been awarded the ISO/IEC 27001 certification for our information security management system. 

Testing AI-powered applications is one of the top priorities for teams deploying machine learning systems at scale. As AI becomes embedded in everyday tools, its behavior becomes harder to control.

This article serves as a step-by-step guide for creating a test automation strategy. It will help you answer why, when, and how to perform automated testing, so you can not only implement it but also overcome its challenges. 

AI doesn’t just fail with bugs. It fails in silence, in bias, and in behavior. That’s why traditional QA won’t cut it anymore.

As software systems get increasingly complex every day, the challenges of effective testing also escalate. Software testing involves handling large datasets, complex workflows, and shorter release cycles. 

Finding a bug is one thing, but documenting it is just as important, if not more so.

Mobile application testing ensures the functional and non-functional quality of mobile application workflows. As more users rely on smartphones for daily tasks, expectations for performance and reliability continue to rise.

Android test automation is the process of using software tools and scripts to execute tests on Android applications automatically. 

Smarter AI starts long before the model is trained. It begins with the quality of the data you feed it. Data that reflects real-world nuance, cultural context, and human behavior is what sets strong systems apart.