Software applications often perform well during short-term testing but encounter issues during continuous use.

Software projects often fail to meet their initial requirements, resulting in expensive post-release fixes and dissatisfied users.

API (application Programming interface) testing plays a crucial role in software testing. On one hand, it allows validation of the correct business logic and ensures that backend—or backstage—systems meet the specifications and the intended user experience.

For a time, desktop applications were declared dead. They became too costly to develop and deploy compared to web apps, which allowed businesses to easily onboard new users, release updates instantly and introduce recurring pricing models rather than one-off purchases.

Software teams today face intense pressure to deliver updates rapidly without compromising quality.

Unit testing is a software testing method in which individual components or units of code are tested in isolation to confirm they function as intended.

As applications increasingly handle vast amounts of data and user traffic, the importance of stress testing is clear. Stress testing helps identify system weaknesses and ensures applications perform reliably under extreme pressure.

Many software applications struggle to handle high user loads, leading to slow performance, crashes, and a poor user experience.

Quality assurance (QA) of systems distributed across multiple servers or regions can not be achieved using localized and single-node testing processes.

Part 1 of this two-part blog serves as the foundation, highlighting the critical role that quality engineering (QE) must play in addressing the business risks associated with the accelerating adoption of artificial intelligence or AI-powered software.