Systems | Development | Analytics | API | Testing

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.

Did you know that almost 80% of failures in performance testing are related to missing basic pre-validation steps before performance testing is done? In the race to meet release dates, many development teams jump into performance testing without knowing that the system is ready for performance testing. The consequences of performance test preparation include inaccurate metrics, wasted infrastructure costs, and inaccurate conclusions relayed around an application’s ability to scale.

Katalon Studio 10.4 introduces a new wave of AI-powered and enterprise-ready capabilities that streamline test creation, maintenance, and analysis. This release focuses on avoiding context switching, simplifying upgrades, and enhancing test intelligence, all designed to make testing faster, smarter, and more reliable.

If you’re using Microsoft Dynamics 365 — whether it’s for enterprise resource planning (ERP), customer relationship management (CRM), or any other business application — data privacy and compliance on the platform should remain a top priority.

Automotive infotainment systems are a core component of the modern driving experience. As these systems expand in features and complexity, the challenges for the software design teams behind them also increase. For automotive leaders, designers, and technicians, staying ahead means.

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.

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.