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SmartBear Updates API Readiness Tools to Accelerate Service Virtualization and Performance Testing

SmartBear Software updated its API tools, ServiceV for API service virtualization and LoadUI NG for API load testing, to accelerate development and testing processes in Agile teams.

Updates to ServiceV enable software teams to rapidly build advanced mocks from real-time API traffic and quickly switch between virtualized “mock” services and actual APIs during diagnostic, load or integration testing in the continuous delivery lifecycle.

“SmartBear's skills in application quality management, and its unique focus on API testing in particular, make it the go-to vendor for such purposes,” says Carl Lehmann of 451 Research in a recent report about API testing. Since the initial release of ServiceV Pro, SmartBear has continued to improve API-related development through strong support for service virtualization based on customer feedback and industry demand.

Developers often use mocks, simple stand-ins for service components, to accelerate the API development cycle. A mock Web service (or virtual API) enables developers to build new functionality and fix bugs faster on parts of a system that otherwise are out of their ability to control, like third party APIs. Not just development, but the processes of testing and user interface design are also sped up by using virtual APIs (“virts”) that can quickly be spun up before the development of the actual API is complete.

New recording features in ServiceV 1.4 allow developers and testers to rapidly build virtual APIs from real-time traffic between an app and one or more APIs. Acting as a high-performance proxy between an app and an actual API, ServiceV Pro virts can now capture real-time traffic as a model and later respond to actions against those endpoints. This feature simplifies the process of synchronizing traditional mocks by hand, typically a time-intensive developer task prone to error especially when a third party service makes changes to their service without prior warning.

Rate-limited APIs are also a particular challenge for testing and development teams in that work is halted when subscription limits are reached, causing teams to miss deadlines in a classic trade-off of time or quality. Because ServiceV 1.4 allows professionals to rapidly craft virtual versions of these APIs and share them amongst their team, third party Web services no longer represent a bottleneck during development and testing, returning the control over making deadlines and minimizing subscription costs.

Control is only as good as how fast a team can exercise it, which is why ServiceV 1.4 also simplifies the process of switching from testing against a virtual API to testing against a real service, either in a QA/staging environment or against production APIs during integration testing. Tests built for one environment can easily be flipped between environments as part of rapid integration testing, saving valuable time during increasingly tight release cycles. Switching comes in particularly useful when running load tests, since third party API involvement can be precisely controlled during a real-time load test, acting as fault isolation.

“Allowing major concurrency bugs or infrastructure bottlenecks to make it to production is a risky and often costly gamble,” said Paul Bruce, API Product Marketing Manager at SmartBear. “Instead, pre-emptive and continuous spot checking through load tests in multiple environments ensures that major performance problems are caught and addressed early on in the software delivery lifecycle. API virtualization compliments all phases of the SDLC, having a powerful impact on reducing time to design, develop, test and diagnose API components in the enterprise.”

ServiceV and LoadUI NG are part of SmartBear’s API Readiness platform, Ready! API, a unified set of testing tools that includes SoapUI NG (functional testing), LoadUI NG (load testing), ServiceV (API service virtualization) and Secure Pro (dynamic API security testing).

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SmartBear Updates API Readiness Tools to Accelerate Service Virtualization and Performance Testing

SmartBear Software updated its API tools, ServiceV for API service virtualization and LoadUI NG for API load testing, to accelerate development and testing processes in Agile teams.

Updates to ServiceV enable software teams to rapidly build advanced mocks from real-time API traffic and quickly switch between virtualized “mock” services and actual APIs during diagnostic, load or integration testing in the continuous delivery lifecycle.

“SmartBear's skills in application quality management, and its unique focus on API testing in particular, make it the go-to vendor for such purposes,” says Carl Lehmann of 451 Research in a recent report about API testing. Since the initial release of ServiceV Pro, SmartBear has continued to improve API-related development through strong support for service virtualization based on customer feedback and industry demand.

Developers often use mocks, simple stand-ins for service components, to accelerate the API development cycle. A mock Web service (or virtual API) enables developers to build new functionality and fix bugs faster on parts of a system that otherwise are out of their ability to control, like third party APIs. Not just development, but the processes of testing and user interface design are also sped up by using virtual APIs (“virts”) that can quickly be spun up before the development of the actual API is complete.

New recording features in ServiceV 1.4 allow developers and testers to rapidly build virtual APIs from real-time traffic between an app and one or more APIs. Acting as a high-performance proxy between an app and an actual API, ServiceV Pro virts can now capture real-time traffic as a model and later respond to actions against those endpoints. This feature simplifies the process of synchronizing traditional mocks by hand, typically a time-intensive developer task prone to error especially when a third party service makes changes to their service without prior warning.

Rate-limited APIs are also a particular challenge for testing and development teams in that work is halted when subscription limits are reached, causing teams to miss deadlines in a classic trade-off of time or quality. Because ServiceV 1.4 allows professionals to rapidly craft virtual versions of these APIs and share them amongst their team, third party Web services no longer represent a bottleneck during development and testing, returning the control over making deadlines and minimizing subscription costs.

Control is only as good as how fast a team can exercise it, which is why ServiceV 1.4 also simplifies the process of switching from testing against a virtual API to testing against a real service, either in a QA/staging environment or against production APIs during integration testing. Tests built for one environment can easily be flipped between environments as part of rapid integration testing, saving valuable time during increasingly tight release cycles. Switching comes in particularly useful when running load tests, since third party API involvement can be precisely controlled during a real-time load test, acting as fault isolation.

“Allowing major concurrency bugs or infrastructure bottlenecks to make it to production is a risky and often costly gamble,” said Paul Bruce, API Product Marketing Manager at SmartBear. “Instead, pre-emptive and continuous spot checking through load tests in multiple environments ensures that major performance problems are caught and addressed early on in the software delivery lifecycle. API virtualization compliments all phases of the SDLC, having a powerful impact on reducing time to design, develop, test and diagnose API components in the enterprise.”

ServiceV and LoadUI NG are part of SmartBear’s API Readiness platform, Ready! API, a unified set of testing tools that includes SoapUI NG (functional testing), LoadUI NG (load testing), ServiceV (API service virtualization) and Secure Pro (dynamic API security testing).

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Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...