Skip to main content

Borland Updates Silk Portfolio with Network Performance Testing

Borland, a Micro Focus company, announced updates to the Borland Silk Portfolio – extending its comprehensive set of quality and performance testing tools for desktop, web, cloud and mobile to include network and collaborative testing.

The latest version will help enable customers to gain a truer understanding of what global end users are experiencing. As a result, customers can be empowered to deliver high-quality applications that run at optimum performance levels quickly and efficiently while improving team collaboration.

Silk Performer has introduced new features to simulate a variety of wired, wireless and mobile network technologies. In addition to mobile bandwidth limitations like 3G, HSPA+ and LTE, the solution also simulates packet drop rate and latency. It enables enterprise organizations to measure the impact of different network conditions like poor antenna signal, high latency on long distance connections and reduced transfer rates, meaning issues can be anticipated and tested out before they occur. This enables them to ensure quality user experience no matter how and from where their users access their applications.

Furthermore, through SilkPerformer Remedy, organizations can now load test Remedy AR System Web solutions, the four-tier system architecture, to simulate IT system users experience reliably on a large scale.

To address the challenge of successful collaboration between technical and business focused users, Silk Test and Silk Central have introduced a collaborative approach to linking business objectives to testing teams called keyword-driven testing (KDT). Developers and testers can now develop the test keywords and let the business analysts and domain experts define the complex business workflows using the keywords, enabling more effective test collaboration. This helps increase productivity and improves testing return on investment (ROI) by reducing the cost and time needed for test design, automation and execution.

Enhancing cloud-based load testing of internal applications behind the firewall, Silk Performer CloudBurst has been extended to include support for testing non-web enterprise applications like SAPGUI, Oracle Applications and Citrix from the cloud using a built-in Virtual Private Network (VPN). This means that enterprises can be confident about safely testing the performance of their in house applications while keeping costs down.

Additional Silk Portfolio updates include:

- Silk Performer – Network emulation has been introduced to simulate mobile bandwidth limitations, packet drop rates and latency, enabling software development teams to measure the impact of different network conditions including poor antenna signal, high latency on long distance connections and reduced transfer rates.

- Silk Test – Keyword-driven testing has been added. Silk Test keeps test design separate from the actual test script to enhance productivity and collaboration between users, and to let key stakeholders, such as line of business owners, easily participate in application testing. Cross-browser support has also been enhanced, enabling users to immediately validate their web application on the latest browser versions when they are released without the need to update Silk Test.

- Silk Central – Keyword-driven testing has been added to aid collaboration across business and technical teams, and improve productivity. The keyword-driven testing integration between Silk Central and Silk Test delivers a seamless transition from manual to automated testing for all users.

- Silk Performer CloudBurst – Support for testing of non-web enterprise applications like SAPGUI, Oracle Applications and Citrix from the Cloud using built-in VPN has been included. This enables enterprises to leverage the elasticity of the Cloud to test the performance of their most critical enterprise applications.

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.

Borland Updates Silk Portfolio with Network Performance Testing

Borland, a Micro Focus company, announced updates to the Borland Silk Portfolio – extending its comprehensive set of quality and performance testing tools for desktop, web, cloud and mobile to include network and collaborative testing.

The latest version will help enable customers to gain a truer understanding of what global end users are experiencing. As a result, customers can be empowered to deliver high-quality applications that run at optimum performance levels quickly and efficiently while improving team collaboration.

Silk Performer has introduced new features to simulate a variety of wired, wireless and mobile network technologies. In addition to mobile bandwidth limitations like 3G, HSPA+ and LTE, the solution also simulates packet drop rate and latency. It enables enterprise organizations to measure the impact of different network conditions like poor antenna signal, high latency on long distance connections and reduced transfer rates, meaning issues can be anticipated and tested out before they occur. This enables them to ensure quality user experience no matter how and from where their users access their applications.

Furthermore, through SilkPerformer Remedy, organizations can now load test Remedy AR System Web solutions, the four-tier system architecture, to simulate IT system users experience reliably on a large scale.

To address the challenge of successful collaboration between technical and business focused users, Silk Test and Silk Central have introduced a collaborative approach to linking business objectives to testing teams called keyword-driven testing (KDT). Developers and testers can now develop the test keywords and let the business analysts and domain experts define the complex business workflows using the keywords, enabling more effective test collaboration. This helps increase productivity and improves testing return on investment (ROI) by reducing the cost and time needed for test design, automation and execution.

Enhancing cloud-based load testing of internal applications behind the firewall, Silk Performer CloudBurst has been extended to include support for testing non-web enterprise applications like SAPGUI, Oracle Applications and Citrix from the cloud using a built-in Virtual Private Network (VPN). This means that enterprises can be confident about safely testing the performance of their in house applications while keeping costs down.

Additional Silk Portfolio updates include:

- Silk Performer – Network emulation has been introduced to simulate mobile bandwidth limitations, packet drop rates and latency, enabling software development teams to measure the impact of different network conditions including poor antenna signal, high latency on long distance connections and reduced transfer rates.

- Silk Test – Keyword-driven testing has been added. Silk Test keeps test design separate from the actual test script to enhance productivity and collaboration between users, and to let key stakeholders, such as line of business owners, easily participate in application testing. Cross-browser support has also been enhanced, enabling users to immediately validate their web application on the latest browser versions when they are released without the need to update Silk Test.

- Silk Central – Keyword-driven testing has been added to aid collaboration across business and technical teams, and improve productivity. The keyword-driven testing integration between Silk Central and Silk Test delivers a seamless transition from manual to automated testing for all users.

- Silk Performer CloudBurst – Support for testing of non-web enterprise applications like SAPGUI, Oracle Applications and Citrix from the Cloud using built-in VPN has been included. This enables enterprises to leverage the elasticity of the Cloud to test the performance of their most critical enterprise applications.

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.