Test Analytics That Turn Data Into Decisions
The Challenge of Test Analytics
Testing tool sprawl
As application stacks evolve, so do the testing tools used to test them, resulting in a multitude of tools being used for different types of testing, which in turn provide different result formats making it difficult to get holistic results across testing pipelines and executions.
Test automations lack metadata
Analytics requires more extensive metadata to run advanced analysis. Managing test automations through CI/CD scripts doesn't enable metadata.
Lack of historical and aggregate visibility
It is hard to see trends over time (test duration, pass/fail rates, flakiness) without keeping and aggregating past execution data. Many systems discard older runs or only show recent data.
Missing performance and resource profiling
Understanding how much time tests take, how much CPU/memory/network/disk usage is involved, and identifying bottlenecks demands instrumentation, collection, and visualization of Kubernetes resource metrics.
Inconsistent test definitions across clusters and releases
Since test definitions are stored as scripts in each pipeline, there is no central catalog of test workflows to draw from to ensure testing consistency. Without consistent tests, the data generated by these seemingly identical tests can't be compared.
Analytics Built for Engineering Leaders
Testkube's Test Insights and Dashboard provide basic to advanced level analytics about testing trends executed by any testing tool in any cluster connected to the central control plane.
The analytics is designed to improve release confidence, reduce costs, and accelerate development cycles for engineering managers.
Testkube Analytics and Insights leverages the data returned from Observability Tooling to perform more advanced analytics.
Turn Test Data Into Action
Tool agnostic analytics
Testkube extracts common quality metrics for any automated test it executes, providing consistent analytics regardless of test tools and how they were executed.
Enriched results database
Each execution is tagged via workflow, status, environment, and labels so filtering and grouping is possible across your entire testing ecosystem.
Built-in dashboarding
Pre-configured dashboards with charts for execution counts, durations, pass/fail rates, resource usage. Filter and group by workflow, labels, environment, or define custom boards.
Efficiency quadrant analytics
Visualize tests by frequency and duration to highlight bottlenecks. Identify tests that run often and take a long time—prime candidates for optimization.
Resource and cost analytics
Track resource usage (CPU, memory, storage, network) per test/workflow execution. Understand test costs and infrastructure overhead to optimize spending.
Workflow health scoring
Detect tests or workflows that are unstable, frequently failing, or intermittent to aid in reliability improvements and prioritize stability work.
Customer Success Story
Real-World Analytics Use Cases
See how Testkube Analytics helps engineering teams make smarter decisions about testing strategy and resource allocation.
- Optimize test suite performance and reduce execution time
- Identify and eliminate flaky tests that erode confidence
- Track resource costs and optimize infrastructure spending
- Monitor quality trends across releases and environments
- Compare testing effectiveness across different teams
Turn Your Test Data Into Your Competitive Advantage
Stop flying blind. See exactly where your testing investment goes, what's working, and what needs optimization. We'll show you how.
Show us your testing landscape
Your tools, pipelines, clusters, and the metrics you wish you had but can't currently track.
See your data unified
We'll demonstrate how Testkube aggregates your test data across all tools and environments into actionable dashboards.
Get your optimization roadmap
Walk away with a clear plan to implement analytics and specific areas where you can reduce costs or accelerate delivery.