Systems | Development | Analytics | API | Testing

SmartBear participated in and won first prize at the ALMathon, the annual hackathon for SAP Partners. The challenge: Create innovative use cases to extend SAP Cloud ALM. So, we integrated Reflect, our AI-powered test automation product, into SAP Cloud ALM to enable users to automate their manual testing process.

Three out of four software leaders think agentic AI will be fully trusted for autonomous testing by 2027, yet only nine percent completely trust AI today. Let’s unpack the data and see what the trust gap tells us about the journey ahead.

It doesn’t matter if you’ve got a team of human QA testers or you’re using one of the many automated testing solutions out there, the truth is this: if you have poor data seeding your QA strategy is going to suffer tremendously. If you do it properly, it enables you to run your whole test suite concurrently, keep your tests focused on the actual functionality they should be testing, and make maintenance easier.

Why do some QA teams consistently deliver reliable and high-quality software, while others toil to identify bugs and experience unstable releases? The real difference often is related to how easily the team is able to use software testing metrics to make measurable decisions. Often, the testing process turns out to be a routine checklist activity – run the tests, publish the results, and move on. However, without useful test metrics, the QA teams simply keep guessing.

AI is evolving faster than the guardrails meant to validate it, leaving organizations exposed to compliance risk, model drift, opaque decision paths, and breakdowns in trust.

Getting the right answer to a technical question, right when you need it, can be the difference between a minor speedbump and a major roadblock. We all know the feeling of being stuck on a specific error or needing a quick "how-to" guide to keep a project moving. That's why we’re excited to announce that the new 24/7 Katalon Support Assistant is now live in production! It's an AI-powered technical support agent and the fastest way to get the help you need, anytime.

Insurance companies face unique challenges in delivering reliable software quickly. Policy updates, claims processing, and regulatory compliance all demand precision and speed. Manual testing alone can create delays, introduce errors, and increase operational risk. That's where test automation for insurance industry comes in. Repetitive regression tests that previously take up so much time can easily be automated.

In today's digital-first world, we face a profound paradox. The technology that connects us, powers our businesses, and fuels innovation is also leaving a significant and growing impact on the environment. Data centres use more energy than certain nations, harmful code wastes gigawatts of electricity, and the ongoing need for more powerful gear means that creation and disposal never stop.

Load testing, performance testing and stress testing are often mixed up, but in today’s CI/CD pipelines and production-grade engineering, they are solving completely different purposed. If you want to: Automate testing within CI/CD pipelines, such as with Keploy, JMeter, Locust, or k6 This guide discusses the difference like Performance testing, Load testing and Stress testing.