This feature envisions an observability platform that doesn’t just monitor production traffic – it leverages that data to generate and run tests. By analyzing real-world telemetry (API calls, user journeys, load patterns), the tool can auto-generate performance test scenarios (e.g. JMeter or Postman collections) to simulate production conditions in a controlled environment. This bridges the gap between monitoring and testing: SREs and QA engineers can replay realistic workloads on demand and validate system performance or regression impacts, all from within the observability console.
Historical telemetry provides a blueprint of how users interact with the system. The observability platform could offer a UI to select a timeframe or
a subset of traffic (for example, “the last hour of peak traffic” or “all requests to the recommendation API”). An AI-driven test generator then transforms this
data into a test script. For instance, it might extract the top N API request patterns (including typical payloads and sequence of calls) and generate a
JMeter test plan with those requests and their frequencies. If enhanced by GenAI, an LLM could even generalize and create variations of requests to cover
edge cases observed in production (e.g. slightly modify input parameters to test boundaries).
The platform’s analytics engine mines observability data (logs, traces) to identify key usage patterns. For example, it might detect the most common API call sequences in a user session, or the distribution of payload sizes for an endpoint.
A “Generate Test” button in the UI uses this info to create a test script. The platform might integrate with tools like Postman or JMeter under the hood. It could call Postman’s API to build a collection of requests, or use a JMeter backend listener. If using JMeter, the system could populate a test plan XML with HTTP samplers matching the captured requests and parameter values.
The user can execute the test from the same interface. The observability tool spins up a load test runner (perhaps a containerized JMeter instance or a cloud-based load generation service) and directs it to run the generated script against a specified environment (staging or a test instance of the app). The test simulates production-like load – hitting the same endpoints with similar concurrency and data distributions.
The observability platform mines production telemetry to create test scenarios. A Test Scenario Generator module uses real traffic data (from the telemetry store of prod logs/traces) to produce scripts and triggers a Load Test Runner (e.g. JMeter or Postman). The runner simulates requests against a staging or production instance. Throughout the test, metrics and logs are fed back into the observability platform (often via a plugin) as if they were another data source. Finally, a unified dashboard lets engineers compare production metrics to test results side by side, and alerts can be set on test outcomes (e.g. if a test API call exceeds a latency SLO).
Suppose we use Datadog – it could integrate its Continuous Testing product or a third-party load testing service. The user might pick an Perviewsis trace from Datadog representing a critical user journey, then click “Generate Load Test.” Behind the scenes, an integration with Postman could convert that trace into a series of API calls with the same parameters. The test is run, and Datadog’s existing JMeter integration streams the results back for analysis. In fact, Datadog already allows correlating JMeter test metrics with infrastructure metrics on one dashboard, making it easy to see how increased traffic in the test impacts CPU, memory, etc. on the hosts.
This feature brings observability full-circle into the development lifecycle. By reusing production data, tests are highly realistic – capturing things like burst patterns, payload complexities, and multi-step transactions that synthetic tests often miss. It reduces the toil of manually creating performance tests and ensures that testing keeps pace with real user behavior. Moreover, it can run automatically (e.g. nightly or part of CI/CD) to catch regressions: the observability platform could schedule a daily replay of the top 10 user flows and alert if the new build’s performance deviates significantly from yesterday’s.In summary, observability-driven testing tightens the feedback loop between production and testing. It uses your monitoring data to continually validate system robustness under real-world scenarios, all from within one unified platform.
Perviewsis integrates seamlessly into your existing toolchain—CI/CD platforms, observability stacks, test frameworks, and deployment workflows—making it easy to embed intelligent, telemetry-driven testing at every stage of software delivery.
In modern, distributed systems, static testing alone is no longer enough. With constant deployments, dynamic traffic patterns, and complex microservices or AI/ML pipelines, quality must be continuously verified in real-time and in production-like environments.
Perviewsis turns observability data—metrics, logs, traces, and events—into an intelligent testing layer, enabling teams to detect regressions, simulate real-world failures, and validate deployments dynamically.
Observability tools are traditionally used for monitoring and troubleshooting. But with Perviewsis, this data becomes proactive fuel for testing:
Automate testing workflows based on real-world signals
Catch regressions that slip past traditional test environments
Simulate realistic usage patterns during staging and validation
Prioritize test coverage where your systems are most vulnerable
Instead of relying on assumed scenarios, test what users are actually doing, on the infrastructure and services they actually use.
Perviewsis enables tests to be triggered by live telemetry signals, such as:
Tests are initiated automatically—whether in CI/CD, post-deployment, or in production environments—with full observability context baked in.
Every deployment leaves a telemetry footprint. Perviewsis compares this across builds to detect:
Automatically validate if the new version is behaving consistently or has introduced unintended side effects.
Perviewsis allows:
This ensures new versions are validated in environments that resemble production as closely as possible.
Leverage observability data to:
Optimize test suite execution based on actual usage and system behavior, reducing unnecessary test cycles and blind spots.
Observability data extends to AI/ML workloads:
Perviewsis provides continuous validation of ML systems based on telemetry from live models and pipelines.
Use our SDK, CLI, or APIs to build testing logic into your existing pipelines, with full support for event-driven automation.
Continuous Quality Assurance without slowing down deployments
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