Software Testing Magazine: Load Testing, Unit Testing, Functional Testing, Performance Testing, Agile Testing, DevOps
In the rapidly evolving landscape of DevSecOps, the integration of Artificial Intelligence has moved far beyond simple code completion. We are entering the era of Agentic AI Automation where speech or a simple prompt performs actions.
The assumption is that AI will handle it all by automate testing, ensure reliability, and keep systems running smoothly. This article explains why it will not. And the consequences of this oversight are already beginning to show.
Anyone who has spent time working on cloud ETL pipelines knows that the biggest problems aren’t the ones that cause your jobs to fail, they’re the quiet ones that slip through unnoticed. AWS Glue is a powerful tool, but it doesn’t tell you when your data is subtly wrong.
Quality at speed is the real benchmark of modern software teams. A reliable QA process protects that speed without trading away user trust or stability. Even strong teams develop blind spots over time. Regression cycles stretch, automation coverage stalls, and production fixes start creeping into every sprint.
Test management is defined by Wikipedia a part of the software testing process that includes the planning of tests and test cases, their execution and the storage and analysis of the tests results. This is achieved also by the integration with requirements management tools, functional software testing tools like Selenium or Cucumber (with the Gerkhin language), continuous integration tools like Jenkins or TeamCity, bug tracking tools like Bugzilla or Mantis, project management tools like Trello, Redmine or JIRA.
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