Systems | Development | Analytics | API | Testing

‘API governance’ can sound like an intimidating concept. When I started as a solutions engineer at SmartBear in 2018, it took me a while to wrap my head around what exactly API governance entails and even longer to be able to explain it clearly and simply to our customers.

Have you ever ended a testing cycle and been disappointed to discover that a critical requirement had not been tested or had even been implemented? If so, you are not alone. It’s a common problem for software projects, especially when they become complicated. Identifying which requirement was tested and validating what was not tested seems like an insurmountable challenge, and that’s where a traceability matrix becomes important.

ISO 26262 is an international standard that addresses the safety of electrical and electronic systems within production vehicles. It encompasses everything from driver assistance and propulsion to infotainment systems (when such systems intersect with the functional safety of the vehicle) and vehicle dynamics control so that software and hardware components perform reliably under all conditions.

Let's talk about everyone's favorite part of mobile development: watching your tests fail just before you head home on a Friday.

In the field of statistics and data analysis, comparing means is a frequent endeavor—whether that means testing the effectiveness of a new drug or analyzing average student scores before and after a training program. In reaching these means accurately, analysts typically use two tests — the Paired t-test and the Unpaired t-test. Although both compare means, they are used in completely different situations. Let’s understand what they are, how they differ, and when to use each.

As we wrap this blog series, let’s take a step back. We’ve explored how agentic QA systems can support traditional software testing: one assistant, one workflow, one metric at a time. But what does it all add up to? The answer isn’t “more automation.” It’s more visibility, more alignment, and more confidence across the software delivery lifecycle.

Manual regression feels simple on the surface. You run your tests again after a code change, check what still works, and you ship. But beneath the surface, it eats time (since manually executing test steps take too much time). It also eats budget, and it quietly slows your release velocity. If you’ve ever thought, “we just need a few days to finish regression,” you’ve likely already paid more than you think. That’s why we created this guide.

Core Banking and Digital Banking transformations are the highest-stakes projects in finance, yet up to 55% of these programmes fail to meet their objectives, budget, or timelines. The problem is rarely the technology or application itself; it is the Assurance Model failing to control the systemic risk of integration, data, and governance. Testhouse has been embedded in recent large-scale core banking migrations, witnessing firsthand where the critical risks lie and, crucially, how to prevent them.