Systems | Development | Analytics | API | Testing

Picture this: You’re creating test cases for a new feature. You have a Jira ticket with text requirements, a Figma mockup from design, a workflow diagram from the architect, and a screenshot from a stakeholder meeting. Traditionally, you’d manually translate all of this into test steps: describing the UI in words, interpreting the diagram, cross-referencing the mockup. But what if your testing tool could “see and “understand” all these artifacts directly, just like you do?

If you find that your team is struggling to get releases out the door, it could be inefficient testing practices. Oftentimes, software teams don’t know what their tests actually cover, or which tests are relevant after each code change — so they run everything. This means spending hours executing full test suites for minor updates or burning through CI/CD resources while bugs slip through untested paths. On top of this, software is always becoming more complex.

We’re entering a phase where AI can draft emails, resolve tickets, summarise complex information, and occasionally present fiction as fact with equal conviction. Generative AI has become incredibly powerful, but in enterprise environments, power without precision quickly becomes a risk rather than an advantage. This is exactly where the shift is happening.

Developers and Quality Assurance (QA) teams utilize many different API testing and validation tools to help them simplify the processes of testing, debugging, and validating APIs in the increasingly API-centric world of software development. Modern teams often combine End-to-End Testing with API-level testing to ensure full workflow reliability.

MLRun 1.10, the latest version of our open source AI orchestration framework, is available today to all users. Iguazio started out as a platform to operationalize enterprise machine learning projects. Though we’ve been through quite a few waves of AI in just a short time, the underlying challenges are the same: getting from experimentation to production remains a major blocker.

For a long time, we spoke about “AI agents” like they were a future concept, something that might eventually book flights, run workflows, or make payments on our behalf.

Modern development strategies rely on continuous integration and continuous delivery (CI/CD) to ship high quality software and updates. A well-designed CI/CD pipeline automates key parts of the software development lifecycle so teams can build, test, and deploy changes faster with fewer errors. To achieve this, development teams need the right CI/CD tools. Before you select your CI/CD solutions, you need to understand CI/CD pipelines and how they work.

In the agile development methodology, the velocity of testing holds the key to delivering the best quality software within the stipulated budget and time constraints. As such, this drives the need to automate the testing process with test orchestration. Most software development teams visualize test automation as a discreet step in the delivery lifecycle instead of viewing it as a designed sequence of steps.

Manual testing is still essential, but it’s often disconnected from automation. The new Manual to Automation capability in Katalon bridges that gap. It converts manual test runs into automated scripts instantly, linking them for unified coverage.

DreamFactory and Postman serve fundamentally different roles in the API development lifecycle. DreamFactory is an API generation and backend platform that automatically creates production-ready REST APIs from databases and other data sources, while Postman is an API development environment focused on testing, documentation, and collaboration. Though both platforms involve API management, DreamFactory generates and deploys APIs while Postman helps developers test and consume them.