Systems | Development | Analytics | API | Testing

A burgeoning cutting-edge technology has been fundamentally transforming how we build automation inside disruptive businesses: agentic AI. The impact of agentic AI is already shaping up to be massive. And agentic adoption is soaring: Kong's Agentic AI in the Enterprise report found that, of those with visibility into their organization’s plans, 90% say their companies are actively adopting AI agents.

In the high-speed world of software development, the label V Software Development can suggest two distinct but connected concepts. On one hand, it denotes software development with the contemporary V programming language—a language intended for simplicity, efficiency, and security. On the other hand, it can symbolize the V-Model Software Development life cycle, a traditional model of software development that is characterized by structure, order, and verification at every phase.

At a previous company, we had over 100 microservices. I’d make what seemed like a simple change to one service and deploy it, only to discover it broke something completely unrelated. A change to the user service would break checkout. An update to notifications would break reporting. We spent more time fixing unexpected bugs than shipping features. The problem was our test scenarios were too simple.

When can a team truthfully say “testing is done”? Have you ever shipped with doubts about whether enough testing actually happened? That hesitation is costly: escaped bugs, hotfixes, and lost customer trust. Test completion answers that question with objective evidence – not just opinions. This blog explains how to define, measure, and document test completion so teams can release with confidence.

AI adoption is accelerating across every industry, from customer service and marketing to finance and retail, and we’re still far from its peak. Many organizations have already begun integrating AI agents into their business operations. A key aspect of this transformation is the use of AI agents, whether purpose-built or off-the-shelf, to assist with or even replace tasks traditionally handled by humans.

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.

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.

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.