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What a control chart tells you
A control chart plots observations in time or sample order against a center line and upper and lower control limits. The limits describe the variation expected when the monitored process remains stable. A point outside a limit, or a systematic nonrandom pattern within the limits, can signal that the process changed and merits investigation. The chart does not identify the cause. NIST’s control-chart overview describes this use of limits and patterns.
In performance testing, a point might represent a test run or a summary of a subgroup of runs. Keep points in chronological order: a chart is intended to reveal changes over time, not just summarize a collection of results. NIST’s software verification and validation reference specifically identifies execution time as an activity to which control charts can be applied. NIST software verification and validation reference
Choose the measure and define each observation
Start with the operational question: are requests getting slower, is throughput changing, or is a particular operation consuming more CPU? Choose a metric that answers that question and define exactly what one plotted point represents. Do not combine unlike units on one ordinary univariate chart; use separate charts for separate measures unless you have a deliberate multivariate method.
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NIST’s NML performance-testing documentation gives examples including maximum and average read/write time, average CPU time for a read/write operation, throughput, and latency (the average time between a write returning and the corresponding message being received by a read). These examples come from that program’s context, not a universal list for all applications. The documentation also notes that clock resolution can affect maximum-time measurements. NIST NML performance measures
- Latency or execution time: specify the operation and statistic, such as median or maximum, rather than recording an ambiguous “speed” value.
- Throughput: define the unit and workload, such as completed requests per second under a stated concurrency.
- CPU or resource time: identify whether the value is per operation, per run, or a system-level summary.
- Counts or proportions: define the opportunity or exposure behind each count, such as errors per fixed number of requests.
Keep the test procedure and conditions sufficiently comparable for the sequence to represent the process you intend to monitor. Record contextual details such as software version, workload, data set, hardware or cloud environment, concurrency, warm-up procedure, and measurement method. These are implementation practices for making the baseline meaningful; the cited NIST performance material emphasizes that results depend on the application and platform.
Build a baseline before monitoring new results
Use a two-phase approach. In Phase I, collect historical observations from the process you intend to monitor, calculate initial limits, and investigate points outside those limits for assignable causes. The objective is not to erase inconvenient data: determine whether a point reflects a known exceptional event, a measurement problem, or genuine process behavior, and document the decision.
Once the baseline is understood and the limits are justified, carry them forward into Phase II monitoring. Plot subsequent comparable results against those fixed limits. Recalculate limits only when the process has materially changed and a new baseline is justified; record what changed and why. Do not silently reset limits after an unfavorable result. NIST’s guidance on control-chart phases
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Control limits are not the same as engineering specifications, an SLO, or an acceptance threshold. A stable service can consistently miss its latency target; an unstable service can sometimes meet that target. Use the control chart to assess stability and compare the measured results separately with the requirement.
Select a chart that fits the data
The right chart depends on how observations are collected, whether they are continuous or counts, and how quickly you need to detect a change. NIST’s chart guidance distinguishes the following families; these are selection cues, not automatic prescriptions. Check assumptions for your metric and sampling design. In particular, standard continuous-data charts often assume approximate normality, which may not fit skewed latency data.
| Data and monitoring goal | Chart family to consider | What it monitors |
|---|---|---|
| Continuous measurements collected in subgroups; monitor subgroup average | X-bar chart, usually paired with an R or S chart | X-bar tracks location (the subgroup mean); R or S tracks within-subgroup variation. |
| Continuous individual observations with no subgroups | Moving average, moving range, or moving standard deviation chart | Recent level or variation using successive individual observations. |
| Small shifts in process location matter | CUSUM or EWMA | Methods designed to detect relatively small shifts. |
| Proportions or counts | P/NP or C/U chart, chosen for the count setup | Binomial proportion/count or Poisson count behavior, as appropriate. |
NIST’s Dataplot documentation explains these chart distinctions and notes that CUSUM and EWMA were developed to detect small shifts of location. NIST Dataplot control-chart documentation
Run the monitoring workflow
- State the question and choose a primary measure. Specify its unit, aggregation, test operation, and the meaning of one plotted point.
- Make the test repeatable. Keep workload, environment, instrumentation, and procedure consistent where possible; log relevant context and preserve observation order.
- Collect historical data. Use observations representative of the process you plan to monitor. Investigate unusual points and establish justified Phase I limits.
- Choose the chart family. Match subgrouping, continuous versus count data, variation monitoring, and desired sensitivity to the measurement design.
- Monitor in time order. Plot every new comparable result against the established limits. Inspect both limit crossings and nonrandom sequences.
- Investigate and document signals. Check software, workload, environment, instrumentation, and test procedure. Record the evidence and any corrective action before deciding whether a new baseline is warranted.
- Assess acceptability separately. Compare results with performance requirements or SLOs in addition to judging whether the process appears stable.
Interpret signals without overreacting
A point above the upper control limit or below the lower limit is evidence that the observation is unusual under the chart’s baseline assumptions. It does not prove a regression or identify its cause. Check for changes in application code, configuration, workload, deployment, machine or network contention, test data, browser state, and measurement tooling. A nonrandom run or trend can also be meaningful even when all points remain inside the limits.
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Signals involve a false-alarm trade-off. NIST’s handbook gives an illustrative Shewhart X-bar case: for a normal process, the probability of an observation falling outside three-sigma limits is 0.0027, corresponding to an average run length of about 371 points before a false alarm when the process has not changed. That figure applies to the stated illustrative conditions; it is not a guarantee for every chart or performance test. Additional run rules can alter both detection behavior and false-alarm frequency. NIST handbook discussion of X-bar chart run length
Performance-test issues that can distort a chart
- Changing conditions: mixing deployments, workload profiles, regions, or machine types can make one baseline represent several processes. Separate or annotate materially different conditions rather than treating them as interchangeable.
- Unstable measurement resolution: very short operations may be close to clock or instrumentation resolution. Record the measurement method and avoid interpreting tiny differences as meaningful without considering its limits.
- Unrepresentative baseline: limits derived from a different workload or environment may generate misleading signals. Re-establish a baseline only for a documented, justified process change.
- Skewed or discrete values: latency distributions can be skewed, and error counts are not continuous measurements. Select a suitable chart and verify assumptions rather than applying an X-bar chart by default.
- Unclear aggregation: changing from per-request values to per-run summaries, or changing subgroup size, changes what the points mean. Keep the definition stable or begin a documented new baseline.
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Every new result looks like a signal
Check that the baseline and current measurements use the same metric definition, workload, environment, subgrouping, and instrumentation. If conditions genuinely changed, do not force the new data into old limits; investigate and establish a documented baseline for the changed process.
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A chart is stable, but the service misses its target
Control limits describe process behavior, not acceptability. Keep the limits for stability monitoring and evaluate the target or SLO separately; a consistently slow process can be statistically stable.
A point crosses a limit, but no cause is obvious
Verify the measurement and test record first, then inspect recent changes across software, infrastructure, workload, and test setup. Treat the point as a reason to investigate, not a verdict. Record what was checked and whether evidence supports a cause.
Small gradual changes are not being detected
Confirm that the chart matches the shift you care about. CUSUM and EWMA are options when relatively small changes in location matter, but their detection and false-alarm behavior depends on the chosen setup.
Further reading
For a broader treatment of statistical process control, NIST’s Dataplot bibliography lists Douglas C. Montgomery’s Introduction to Statistical Quality Control, Fourth Edition. It is a general SPC reference, not a performance-testing manual. NIST Dataplot references
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Frequently Asked Questions
Does an in-control chart mean my application is fast enough?
No. It indicates statistical stability under the chart’s assumptions; compare performance with a separate target or acceptance criterion.
Can I use a control chart for latency?
Yes, if the observations and chart method suit the latency data and the test conditions are comparable. Check whether the distribution and measurement resolution fit the chart assumptions.
Should I remove a point outside the limits?
Not automatically. Investigate and document the cause; exclude or treat it differently only when the measurement or process context justifies that decision.
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