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Jetpack Macrobenchmark measures performance across test-driven Android app journeys, such as a cold launch, opening a screen, scrolling a list, or playing an animation. Put benchmark code in a separate com.android.test module, drive the app from outside its process, and measure only the actions that answer your performance question. Choose startup or frame metrics to fit the journey, then compare results only when the device, startup state, and compilation conditions match.
What Macrobenchmark measures—and what it does not
Macrobenchmark runs tests that interact with a target app and collect performance metrics across those interactions. It is suited to user-visible flows such as launching from the launcher or a notification, navigating to a screen, scrolling, or sustaining an activity such as music playback. Android’s performance-measurement guidance recommends identifying important journeys and combining automated collection with manual investigation of individual runs.
The benchmark runs in a separate process from the app under test. Its code belongs in a com.android.test module, not in the app module; the target app must be profileable to provide detailed trace information without affecting its performance. See Android’s Macrobenchmark setup guide.
Choose a journey that answers one question
Start by stating what you want to learn: how quickly the app appears after launch, whether a scrolling screen produces slow frames, or how long a specific portion of work takes. Keep the benchmark focused on a representative flow rather than unrelated actions that make the result harder to interpret.
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- Launch: measure a cold, warm, or hot startup with
StartupTimingMetric. - Scrolling, transitions, or animation: perform the interaction and collect
FrameTimingMetricdata. - A specific operation: use a named trace section with
TraceSectionMetricwhen the question concerns that section’s duration.
A frame benchmark does not have to scroll: it can capture frames produced by an animation or other UI work. You can pass more than one metric to a benchmark, but at least one is required. Metric details and platform qualifications are in Android’s Macrobenchmark metrics guide.
Separate reaching the screen from measuring it
If the question concerns a screen deeper in the app, use setup to reach it and reserve the measured block for the screen’s relevant work. That way, the result reflects the target activity—such as rendering and scrolling a list—rather than also including unrelated navigation. Android’s app-control guide describes this separation with setupBlock and measureBlock.
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Because the benchmark runs outside the app process, it cannot directly call an in-process object such as a NavController. Drive the app as a user would, using UI Automator or another mechanism that can control it from the benchmark process. For Compose, expose stable test tags as resource IDs using the documented semantics setting, then locate those elements from the benchmark and perform the action.
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Build the benchmark around the journey
MacrobenchmarkRule provides measureRepeated. The call specifies the target package, metrics, iteration count, and the interactions to run. Here is a structural example for a launch journey; replace the package and actions with the app’s actual flow:
@RunWith(AndroidJUnit4::class)
class LaunchBenchmark {
@get:Rule
val benchmarkRule = MacrobenchmarkRule()
@Test
fun launch() = benchmarkRule.measureRepeated(
packageName = "com.example.app",
metrics = listOf(StartupTimingMetric()),
iterations = 5,
startupMode = StartupMode.COLD
) {
pressHome()
startActivityAndWait()
}
}The iteration count shown is only an example, not a guarantee of statistical reliability. Adapt the test structure to the APIs and dependency versions in your project, and keep the actions inside the measured portion aligned with the performance question. Android’s overview documents the rule and journey example.
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Choose metrics that match the result you need
| Question | Metric | What it reports | Important qualification |
|---|---|---|---|
| How quickly does the app show its first screen? | StartupTimingMetric |
timeToInitialDisplayMs, from launch intent to the destination activity’s first frame; output includes minimum, median, and maximum. |
Use the median for typical startup. timeToFullDisplayMs is also available when the app supplies the fully drawn signal; its behavior can depend on Android version and app reporting. |
| Are frames slow during scrolling, animation, or another interaction? | FrameTimingMetric |
frameDurationCpuMs and distributions including P50, P90, P95, and P99. Higher percentiles reveal slower frames. |
frameOverrunMs is available on Android 12 (API 31) and later. Positive overrun means a frame missed its deadline and may correspond to visible jank. |
| How long does a named portion of code take? | TraceSectionMetric |
Timing for matching named trace sections. | Android marks this metric experimental. By default, it selects the first matching instance and filters to the target package unless configured otherwise. |
Consult the official metrics reference for startup and frame behavior and the Views metrics guide for trace-section details.
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Startup mode changes what a startup result means. Android documents three modes: COLD measures from a process killed between setup and measurement; WARM recreates activity state while retaining the process; and HOT represents a process and activity that are already more available. Select the mode that reflects the user experience you want to measure. Do not treat results from different modes as directly comparable.
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Compilation state also affects results. Android describes CompilationMode.DEFAULT as installing a Baseline Profile when available on Android 7 (API 24) and later, alongside other modes for varying precompilation and JIT conditions. Benchmark setup resets compilation state to reduce interference. The official overview notes a version-dependent caveat: Android 14 (API 34) and later can persist state when benchmarking, while earlier versions may require reinstalling the APK; it also identifies CompilationMode.Ignore as a workaround when compilation is controlled separately. Check the current run and compilation guidance before relying on that operational detail.
Make results comparable and investigate outliers
Record the conditions that define each run so another developer can tell what changed. Include:
- Device model and Android version
- App build and benchmark build
- Startup mode and compilation mode
- Journey setup and measured actions
- Iteration count
Compare builds under matched conditions. For startup, examine the median rather than choosing the fastest run; for frame behavior, inspect the percentile distribution, especially the slower tail. Automated output helps identify regressions, but manually inspect individual runs to investigate suspicious results, as Android’s measurement guidance recommends.
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