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What Macrobenchmark measures—and what it needs
Macrobenchmark runs test-driven journeys against your app from a process separate from the target app. Android requires its benchmark code to live in a separate com.android.test module; the target app must be profileable to enable detailed trace information without affecting performance. The benchmark uses MacrobenchmarkRule and its measureRepeated method to specify the target package, metrics, iteration count, and actions. See Android’s Macrobenchmark overview.
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This setup is intended for repeatable, lab-style measurement of flows such as launch, opening a frequently used screen, scrolling a list, navigating between screens, or running an animation. It does not replace field performance monitoring or investigation of an individual trace; Android recommends manually inspecting individual test runs alongside automated collection. See the overview of measuring app performance.
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Choose a journey that answers one performance question
Start by naming the user-visible concern: slow cold launch, delayed readiness after launch, a stuttering list, or a laggy transition. Then build the benchmark around the smallest representative flow that exposes it. A journey can capture a whole flow, or it can prepare the app at a deeper screen and measure only the screen’s work.
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- Launch: Start from the launcher or another real entry point, such as a notification, if that is the path you want to assess.
- Scrolling: Open a representative data list and perform a consistent scroll.
- Navigation or transition: Trigger the screen change users experience and measure its frames or relevant code work.
- Sustained interaction: Measure a longer flow, such as navigation or music playback, when its ongoing performance is the concern.
The journey should reflect actual app behavior. A benchmark that reaches the right screen but performs a different action from the one users experience may answer the wrong question.
Separate setup from measured actions
Use setupBlock to establish the starting state and reach a deep screen; use measureBlock for the action whose cost you want to capture. For example, if the question is whether a product list scrolls smoothly, prepare the app at that list in setup, then measure the scroll. If the question is launch time, launch itself belongs in the measured flow.
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- Make setup repeatable. Establish the same starting screen and data state for each iteration. Avoid relying on leftovers from a previous run.
- Drive the app externally. Because the benchmark runs out of process, it cannot directly manipulate an in-process
NavController. Use UI Automator or another mechanism that can control the target app from the benchmark process. - Expose stable Compose targets when needed. For Compose UI, expose stable test tags as resource IDs using the documented semantics setting, then locate the tagged element from the benchmark and perform the user action.
- Keep the measured block focused. Include only the interactions needed to reproduce the performance concern, rather than setup navigation that would contaminate the measurement.
Android documents this separation and external control in Control your app from Macrobenchmark. Its codelab also notes that Macrobenchmark reinstalls or restarts the target app and recommends state-independent interactions; do not assume that persistent UI state will survive between runs.
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At least one metric is required, and a benchmark can pass multiple metrics together. Choose based on whether the concern is app startup, frame smoothness during interaction, or a specific instrumented trace section. Android’s metric definitions and supported outputs are in Capture Macrobenchmark metrics.
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| Question | Metric | What to interpret |
|---|---|---|
| How long until the first screen appears? | StartupTimingMetric |
timeToInitialDisplayMs measures from the launch intent until the destination activity’s first frame. Compare the median as the typical startup result; the output also includes minimum and maximum. |
| How long until the app says it is fully drawn? | StartupTimingMetric |
timeToFullDisplayMs uses the app’s fully drawn signal. Its meaning and availability can depend on Android version and whether the app reports that signal. |
| Are scrolling, transitions, or animations producing slow frames? | FrameTimingMetric |
Inspect frameDurationCpuMs and the P50, P90, P95, and P99 distributions. On Android 12 (API 31) and later, the metric also reports frameOverrunMs; positive overrun means a frame missed its deadline and may correspond to visible jank. |
| How long does a named instrumented code section take? | TraceSectionMetric |
Measures matching named trace sections. It is marked experimental, selects the first matching instance by default, and filters to the target package unless configured otherwise. |
Frame timing is useful for more than scrolling: it can capture frames produced by animations or other UI work. For frame metrics, higher percentiles help reveal the slowest frames that a median can obscure; they do not by themselves establish how often users encounter the same behavior in the field. For trace-section details, see Android’s Macrobenchmark metrics for Views.
Make startup and compilation conditions explicit
Startup mode changes what a launch result represents. Android defines COLD, WARM, and HOT startup modes. Cold startup measures with the process killed between setup and measurement; warm startup recreates activity state while retaining the process; hot startup represents a process or activity that is already more available. Choose the mode that matches the user situation you want to study, and do not compare results from different modes as if they were equivalent.
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Compilation mode matters too. Android documents CompilationMode.DEFAULT as installing a Baseline Profile when available on Android 7 (API 24) and later. Other modes let you vary precompilation and JIT conditions. Benchmark setup resets compilation state to reduce interference, but operational behavior differs by Android version: the guide says Android 14 (API 34) or later can persist state when benchmarking, while earlier versions may require reinstalling the APK. It notes CompilationMode.Ignore as a workaround when compilation is controlled separately. Check the current Android guide before relying on that operational detail: customizing and running benchmarks.
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Record conditions before comparing results
A benchmark number only describes the run that produced it. Keep these conditions with each result so a later comparison does not silently mix different workloads or environments:
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- Device model and Android version
- App build and benchmark build
- Journey, including setup state and measured actions
- Startup mode and compilation mode
- Metric or metrics collected
- Iteration count
Use a device that fits the project’s target Android versions and test requirements; Android’s guidance does not specify a required phone model. Keep those conditions matched when comparing builds, then inspect individual runs and traces when an aggregate result raises a question. An iteration count is an input to the benchmark, not a guarantee of statistical reliability; do not treat a small change as meaningful without considering variability and the run conditions.
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