Systems | Development | Analytics | API | Testing

With the speed of software development today, security is no longer an after-thought, it is a requirement. Cyberattacks are increasing, and the discovery of vulnerabilities in source code can lead to significant risk for organizations. Code scanning, or scanning code for vulnerabilities, is the answer. Code scanning is an essential practice in DevSecOps in which developers find, fix, and secure code before it reaches a production environment.

Imagine deploying a sparkling new feature in your app that performs flawlessly in testing, but when it goes live, it all comes crashing down. Orders won’t process, data won’t sync, and ultimately, the users encounter dead ends. What is wrong? Most typically, the problem is with the APIs not doing what they are supposed to. This is exactly what API functional testing guards against.

When you add AI to your product, the hardest part is not building the feature but making sure it works safely, reliably, and as intended in the real world. Hemraj Bedassee , Delivery Excellence Practitioner,

Taking place in London and Nashville, Tricentis Transform brings together quality engineering leaders and professionals to hear insights from celebrity keynote speakers, Tricentis executives and customers, and industry experts. They’ll share perspectives on topics ranging from delivering better and faster software with AI to building performance into quality engineering strategies.

OAuth 2.0 is the leading open authorization framework that enables secure delegated access to protected resources. From traditional web apps and browser-based apps to native apps and desktop applications, OAuth allows client apps to grant access on a user’s behalf without exposing login credentials, enabling powerful third-party applications, custom data flows, and powerful user experiences. However, while OAuth is secure, it’s not always fast.

As Artificial Intelligence (AI) becomes deeply embedded in decision-making across fraud detection, chatbots, and virtual assistants, trust in AI agents is now critical. Users and stakeholders need clear assurance that these systems will behave fairly, clearly, evidently, and reliably in all situations. However, building that trust does not happen by chance; it requires smarter testing strategies specifically designed for the non-deterministic and robust nature of AI.

You're in the Sprint Review, and the team is feeling pretty good about the new feature, it’s done, the CI (Continuous Integration) pipeline is green, and they have a Friday release planned. Things are going according to plan. Then something worse happens. A test fails. But no one has an explanation. It passed yesterday. It works on my machine. Perhaps it is just the test environment again? You rerun it; green. Rerun it; red. The inconsistency starts introducing doubt. Is it an actual problem?

In software product development, many teams tend to ignore quality metrics and focus more on quantity. Such teams face challenges when building for production. They end up pushing to production very low-quality software that is filled with bugs. These bugs alone irritate and drive away product users. In 2022, research done by the Consortium for Information and Software Quality (CISQ) revealed that the cost of poor software quality in the US has grown to at least $2.41 trillion.