Oracle announced self-service testing capabilities through Oracle Enterprise Manager 12c.
The solution is intended to help software development and Quality Assurance (QA) organizations deliver higher quality applications, while significantly enhancing testing efficiency and reducing the duration of testing projects.
It also allows developers to spend more time on actual testing and less on peripheral activities that accompany the testing process, such as setting up hardware and installing testing tools, test assets and application under test.
Organizations can also expect significant reduction in hardware, power and cooling costs typically required to support testing and staff time spent on testing projects.
Cloud Based Self-Service Testing Provides Better Efficiency and Agility
The Testing-as-a-Service solution offers test lab management, automatic deployment of complex multi-tier applications, rich application performance monitoring, test data management and chargeback in a unified workflow.
Features include:
- Self-service portal for executing application load tests: making it easier for Development and QA professionals to create, manage and execute tests.
- End-to-end automation of test processes: from provisioning of multi-tier applications to test tools and test scripts that allow users to significantly decrease the time needed to setup a complete test environment.
- Sharing of cloud hardware resource pools: with complete isolation to ensure security, enabling users to maximize hardware utilization while abiding by security policies.
- Integrated and rich application monitoring and diagnostics: for middle tier and data tier, to help ensure detection of bottlenecks and problems for the application under test, reducing test cycles without compromising application quality and improving overall organizational efficiency.
- Resource metering and chargeback: for tests that require to be charged to cost centers, the metering capabilities allow for tracking and charging for compute resources used.
Supporting Quotes
"Organizations are spending as much as 50 percent of their QA time with non-test related activities like setting up hardware and deploying applications and test tools,” said Leng Tan, Oracle senior vice president, Product Development. “The Oracle Enterprise Manager software testing cloud capability can transform QA organizations by allowing testers to focus on their main task of designing and executing test cases, to ensure that the application under test meets its quality goals, rather than spending valuable time on non-test related activities."
The Latest
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.
The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...