Get a clear overview of how fast your testing efforts are moving in a single place with the Languages report. Track key indicators to determine if they are sending any signals that require action or further investigation.
Find the report in the Testlio platform under Reports > Speed. You can filter the data for a specific date range and/or specific workspaces.
This report provides visibility into testing speed by surfacing trends in test duration and response times. Through the Speed report, teams can identify patterns that impact test efficiency, uncover opportunities to streamline execution, and make informed decisions that support faster, more effective testing cycles. This improves operational transparency, reduces reliance on manual tracking, and enables better planning and prioritization across test runs.
Monitor Execution
Indicator | What It Tracks | What Signals it Might Bring |
Total Test Execution | The total number of tests executed during the selected period. Automatically compares with the number during the prior period. | This metric helps teams understand overall test workload, track efficiency trends over time, and optimize how testing resources are allocated.
|
Total Execution Time | The total time spent executing all tests during the selected period. Automatically compares with the number during the prior period. | This metric helps teams monitor overall test workload, analyze efficiency trends, and manage resource allocation.
|
Avg. Execution Time per Test | The average time it took to execute a single test during the selected period. Automatically compares with the number during the prior period. | This metric helps teams assess execution speed and track efficiency trends across test runs.
|
Test Execution Overview | A visual comparison of the total test execution, total execution time, and average execution time per test over time during the selected period. | Use this chart to track individual indicators over time. |
Track Efficiency
Indicator | What It Tracks | What Signals it Might Bring |
Avg. Turnaround Time | The average time between when a test run is scheduled and when it is marked as finished during the selected period. Automatically compares with the number during the prior period. | This metric helps teams evaluate the efficiency of test execution workflows and track how quickly results are delivered. |
Parallel Execution Efficiency | The percentage of time saved by executing tests in parallel rather than sequentially during the selected period. Automatically compares with the number during the prior period.
See more about parallel execution efficiency.
| This metric helps teams assess how effectively resources are being utilized and how well the test infrastructure supports parallelization.
|
Avg. Queue Wait Time | The average time devices spent in the queue before test execution began during the selected period. Automatically compares with the number during the prior period.
The wait time is calculated as the difference between the test run start time and the device run start time.
| This metric helps assess how efficiently test runs are being scheduled and how well available resources are being utilized.
|
Test Execution Efficiency | A visual comparison of the total turnaround time, parallel execution efficiency, and queue wait time over time during the selected period. | This chart helps teams identify patterns, assess system performance, and detect emerging bottlenecks that may impact testing speed and resource utilization. |
Avg Turnaround Time with Benchmarks | A visual representation of the turnaround time over time. The chart includes benchmarks from all Testlio testing, with lines for the 25th, 50th (median), and 75th percentiles. | This chart helps you track where you stand with your testing efficiency as compared to other companies testing with Testlio. |
More on Parallel Execution Efficiency
Example: Visualizing Parallel Efficiency
In this example, there are three tests that take different amounts of time:
Test A: 20 min
Test B: 10 min
Test C: 5 min
A test run included these tests with some overlap
Test A: 12:00–12:20
Test B: 12:05–12:15
Test C: 12:25–12:30
If these tests had run sequentially (one after another), the total time would have been: 20 min (A) + 10 min (B) + 5 min (C) = 35 minutes
In reality, with tests running in parallel, the actual run took: From 12:00 (start of A) to 12:30 (end of C) = 30 minutes
That means 5 minutes were saved by running some tests in parallel.
➡️ Parallel Execution Efficiency = (35 - 30)/35 = 14.3%
This means the run was 14.3% faster than if the tests had run sequentially.
📊 Why It Matters
A higher percentage = better use of parallel execution → faster feedback
A lower percentage = less efficient use of parallelization → possible delays
⚠️ How Efficiency Can Be Negative
If there are gaps between tests (time where no tests are running), the total run time might actually be longer than if the tests had run back-to-back without any parallelization.
🔻 Example: Negative Efficiency
Three tests take 10 minutes each.
But they are scheduled poorly with long pauses in between them.
The full run ends up taking 40 minutes instead of 30
→ In this case, the efficiency is negative: (30 - 40) / 30 = -33%
This indicates an inefficient run setup. It might be due to infrastructure delays, poor parallelization logic, or test distribution issues.