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SmartBear Announces Support for Integrated GUI and API Testing

SmartBear Software announced new versions of TestComplete and QAComplete.

With this release, TestComplete and QAComplete are now integrated with SoapUI, the most used API testing tool in the world, as well as with SoapUI NG and ServiceV, tools that are part of the Ready! API family, a fully integrated and extensible platform for building reliable, scalable and secure APIs. Customers deploying the new versions are able to design automated functional tests at the Graphical User Interface (GUI) as well as API level.

Most applications today include numerous calls to APIs. If a tester wants to make sure their application works, they not only need to test the application’s GUI, but also ensure the APIs dictating the information presented on the GUI work as expected. In a modern banking application for example, the tester may be required to go below the GUI and test if customer information accessed from mainframe using Web services returns the right customer details. The tester may even be required to test third-party APIs like Apple maps to confirm the nearest bank location presented on the GUI is correct. An ability to go beyond the GUI layer and drive automated tests at the underlying API layer enables testers to effectively test for business logic, driving the GUI and thereby preventing rework associated with late discovery of defects. To successfully test applications both at GUI and the API level, an integrated testing approach is required.

But this integrated testing approach may always not be enough. Challenges arising due to dependency between the GUI and API can become even more pronounced in an agile environment. Early sprints in agile development often have an unfinished Web services component. It can thereby become difficult to accurately test how the GUI would behave in production when the Web service goes live. If a different team is developing the API than the one testing it, a lot of back and forth is involved in the process, making testing continuously in an agile environment tedious and long. Hence, testing is often pushed back in the development cycle, altering the quicker delivery deadlines necessitated. To solve these problems, other than having an integrated GUI and API testing solution, an access to virtual APIs that enable testers to work on APIs still in development is crucial.

“In the last two years, it has become clear that traditional waterfall development and testing across the software lifecycle is increasingly outdated, and that agile methods are more applicable,” said Raul Castañon-Martinez, Senior Analyst, Enterprise Mobility at 451 Research. “There is a clear trend towards a compressed lifecycle and stronger collaboration across the software development lifecycle (SDLC). Developers and testers increasingly require tools that will allow a compressed development lifecycle and concurrent development and collaboration across different teams, where at all stages stakeholders are as close as possible to what the end user will experience.”

With TestComplete and QAComplete integration to SoapUI NG and ServiceV as well as SoapUI, SmartBear allows developers and testers to:

- Reduce dependencies between the GUI and API development and testing by allowing testers to simulate Web services that are still in development or not available

- Perform end-to-end automated testing by going beyond the GUI to automate tests that invoke APIs or other Web services

- Quickly identify problems and reduce debugging time by driving tests at the API level

- Apply test management practices such as establishing traceability between requirements, tests and defects for APIs and ensure proper API and functional test coverage exists for requirements

- Reduce dependence and wait times between different testing teams by providing instant accessibility to changes made to tests at the GUI, API and service levels

- Get a single report on all testing efforts, including manual, automated, Selenium and API to better prioritize testing efforts

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SmartBear Announces Support for Integrated GUI and API Testing

SmartBear Software announced new versions of TestComplete and QAComplete.

With this release, TestComplete and QAComplete are now integrated with SoapUI, the most used API testing tool in the world, as well as with SoapUI NG and ServiceV, tools that are part of the Ready! API family, a fully integrated and extensible platform for building reliable, scalable and secure APIs. Customers deploying the new versions are able to design automated functional tests at the Graphical User Interface (GUI) as well as API level.

Most applications today include numerous calls to APIs. If a tester wants to make sure their application works, they not only need to test the application’s GUI, but also ensure the APIs dictating the information presented on the GUI work as expected. In a modern banking application for example, the tester may be required to go below the GUI and test if customer information accessed from mainframe using Web services returns the right customer details. The tester may even be required to test third-party APIs like Apple maps to confirm the nearest bank location presented on the GUI is correct. An ability to go beyond the GUI layer and drive automated tests at the underlying API layer enables testers to effectively test for business logic, driving the GUI and thereby preventing rework associated with late discovery of defects. To successfully test applications both at GUI and the API level, an integrated testing approach is required.

But this integrated testing approach may always not be enough. Challenges arising due to dependency between the GUI and API can become even more pronounced in an agile environment. Early sprints in agile development often have an unfinished Web services component. It can thereby become difficult to accurately test how the GUI would behave in production when the Web service goes live. If a different team is developing the API than the one testing it, a lot of back and forth is involved in the process, making testing continuously in an agile environment tedious and long. Hence, testing is often pushed back in the development cycle, altering the quicker delivery deadlines necessitated. To solve these problems, other than having an integrated GUI and API testing solution, an access to virtual APIs that enable testers to work on APIs still in development is crucial.

“In the last two years, it has become clear that traditional waterfall development and testing across the software lifecycle is increasingly outdated, and that agile methods are more applicable,” said Raul Castañon-Martinez, Senior Analyst, Enterprise Mobility at 451 Research. “There is a clear trend towards a compressed lifecycle and stronger collaboration across the software development lifecycle (SDLC). Developers and testers increasingly require tools that will allow a compressed development lifecycle and concurrent development and collaboration across different teams, where at all stages stakeholders are as close as possible to what the end user will experience.”

With TestComplete and QAComplete integration to SoapUI NG and ServiceV as well as SoapUI, SmartBear allows developers and testers to:

- Reduce dependencies between the GUI and API development and testing by allowing testers to simulate Web services that are still in development or not available

- Perform end-to-end automated testing by going beyond the GUI to automate tests that invoke APIs or other Web services

- Quickly identify problems and reduce debugging time by driving tests at the API level

- Apply test management practices such as establishing traceability between requirements, tests and defects for APIs and ensure proper API and functional test coverage exists for requirements

- Reduce dependence and wait times between different testing teams by providing instant accessibility to changes made to tests at the GUI, API and service levels

- Get a single report on all testing efforts, including manual, automated, Selenium and API to better prioritize testing efforts

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.