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Infovista Announces User Experience Testing of OTT and 5G

Infovista announced new user experience testing solutions for the most resource intensive – and latency sensitive – OTT applications and interactive 5G services.

Combining various generic testing techniques, real live service traffic pattern emulations and machine learning (ML) algorithms, Infovista enables mobile operators to fully test not only their networks, but also the user experience of any native or OTT applications and services running over them. The Infovista user experience testing portfolio includes sQLEAR, Infovista’s VoLTE and VoNR voice quality testing solution, and new generic testing solutions for OTT voice services, OTT video streaming, and interactive services including e-gaming, remote drone control and video conferencing.

Operators can now test the user experience of high bandwidth, low latency 5G services such as e-gaming using Infovista’s new generic pattern profiling. By emulating the traffic patterns of highly interactive and intensive services, such as the First-Person Shooter (FPS) game genre (e.g. Counter-Strike), Infovista’s testing solutions validate if the network can deliver a great user experience for subscribers using this type of service. Real-time emulation of traffic based on adaptable network conditions enables gaming KPI measurements to be mapped to user experience scores on the service’s interactivity quality. This allows operators to determine both network readiness and any potential improvement actions needed to improve the end user experience.

Infovista’s new generic framework for OTT media testing solves the key challenge with testing OTT media today, namely that parameters such as how to login, or the layout and behavior of the application can change without any notice, and can differ between devices, platforms, countries and even networks. By providing user interface (UI) automation when setting up the tests, while the test methodology and KPIs remain generic, Infovista enables operators to test the generic framework regardless of what OTT media application/service is being used. This saves operators time when testing a multitude of OTT media applications and allows them to quickly test any new application with consistency and confidence.

Infovista now enables operators to generically test OTT voice and video streaming across the large variety of applications, codecs and clients by using a generic client to mimic the behavior of an OTT voice client (e.g. WhatsApp) and/or video streaming client (e.g. video on demand category like Netflix). Infovista’s voice quality ML-based predictor, sQLEAR, uses a generic OTT voice client design based on one of the most commonly used OTT voice apps, WhatsApp. This significantly reduces both cost and time to market of new OTT voice services.

Dr. Irina Cotanis, Technology Director, Network Testing at Infovista explains how using Infovista’s generic testing techniques enables operators to efficiently test user experience for all OTT apps running over their network, while reserving app specific testing for only those apps or services identified as requiring special care and attention: “If we look at the variety and diversity of popular OTT applications, we can see it is impossible to test them all. The most eloquent example is the most demanding and popular 5G service, mobile cloud gaming, for which the multitude and variety of game genres make it clear that there is no point even trying to optimize your network for all the different types of games. Thanks to the multitude of OTT apps and the ever-expanding range of devices, it’s simply not practical or financially viable to test every device, every OTT app and every interactive service. Instead, by testing the network against the key parameters of the most demanding and/or most commonly used OTT application/service, we can give operators the confidence they need to not just deliver a network capable of supporting less intensive and/or less common OTT apps and services, but make user experiences promises, safe in the knowledge that their network will deliver.”

Infovista’s TEMS Network Testing Portfolio enables network and services performance quality evaluation, troubleshooting and optimization by measuring and benchmarking end user experience. For Network Operators and Regulators, TEMS delivers the ability to walk test, drive test, and dynamically analyze service performance under real-life conditions—indoors, outdoors, and around the clock.

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Infovista Announces User Experience Testing of OTT and 5G

Infovista announced new user experience testing solutions for the most resource intensive – and latency sensitive – OTT applications and interactive 5G services.

Combining various generic testing techniques, real live service traffic pattern emulations and machine learning (ML) algorithms, Infovista enables mobile operators to fully test not only their networks, but also the user experience of any native or OTT applications and services running over them. The Infovista user experience testing portfolio includes sQLEAR, Infovista’s VoLTE and VoNR voice quality testing solution, and new generic testing solutions for OTT voice services, OTT video streaming, and interactive services including e-gaming, remote drone control and video conferencing.

Operators can now test the user experience of high bandwidth, low latency 5G services such as e-gaming using Infovista’s new generic pattern profiling. By emulating the traffic patterns of highly interactive and intensive services, such as the First-Person Shooter (FPS) game genre (e.g. Counter-Strike), Infovista’s testing solutions validate if the network can deliver a great user experience for subscribers using this type of service. Real-time emulation of traffic based on adaptable network conditions enables gaming KPI measurements to be mapped to user experience scores on the service’s interactivity quality. This allows operators to determine both network readiness and any potential improvement actions needed to improve the end user experience.

Infovista’s new generic framework for OTT media testing solves the key challenge with testing OTT media today, namely that parameters such as how to login, or the layout and behavior of the application can change without any notice, and can differ between devices, platforms, countries and even networks. By providing user interface (UI) automation when setting up the tests, while the test methodology and KPIs remain generic, Infovista enables operators to test the generic framework regardless of what OTT media application/service is being used. This saves operators time when testing a multitude of OTT media applications and allows them to quickly test any new application with consistency and confidence.

Infovista now enables operators to generically test OTT voice and video streaming across the large variety of applications, codecs and clients by using a generic client to mimic the behavior of an OTT voice client (e.g. WhatsApp) and/or video streaming client (e.g. video on demand category like Netflix). Infovista’s voice quality ML-based predictor, sQLEAR, uses a generic OTT voice client design based on one of the most commonly used OTT voice apps, WhatsApp. This significantly reduces both cost and time to market of new OTT voice services.

Dr. Irina Cotanis, Technology Director, Network Testing at Infovista explains how using Infovista’s generic testing techniques enables operators to efficiently test user experience for all OTT apps running over their network, while reserving app specific testing for only those apps or services identified as requiring special care and attention: “If we look at the variety and diversity of popular OTT applications, we can see it is impossible to test them all. The most eloquent example is the most demanding and popular 5G service, mobile cloud gaming, for which the multitude and variety of game genres make it clear that there is no point even trying to optimize your network for all the different types of games. Thanks to the multitude of OTT apps and the ever-expanding range of devices, it’s simply not practical or financially viable to test every device, every OTT app and every interactive service. Instead, by testing the network against the key parameters of the most demanding and/or most commonly used OTT application/service, we can give operators the confidence they need to not just deliver a network capable of supporting less intensive and/or less common OTT apps and services, but make user experiences promises, safe in the knowledge that their network will deliver.”

Infovista’s TEMS Network Testing Portfolio enables network and services performance quality evaluation, troubleshooting and optimization by measuring and benchmarking end user experience. For Network Operators and Regulators, TEMS delivers the ability to walk test, drive test, and dynamically analyze service performance under real-life conditions—indoors, outdoors, and around the clock.

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Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

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