Quick answer: Check the pack’s delivery_type in its build.gradle. install_time packs are bundled at install; on_demand packs need a code request via AssetPackManager before files exist on disk.

You moved your game’s level data into a 400 MB asset pack to keep the base APK small. Install via Play internal testing — the game crashes immediately because the level data files aren’t found. The pack was supposed to ship with the install.

Three Delivery Types

Each asset pack in build.gradle declares a delivery type:

// level_data/build.gradle
apply plugin: 'com.android.asset-pack'

assetPack {
    packName = "level_data"
    dynamicDelivery {
        deliveryType = "install-time"   // or "fast-follow" or "on-demand"
    }
}

Mistyping the value silently defaults to on-demand on some Gradle versions — the pack ships but isn’t delivered without a code request.

Access from Code

Regardless of type, retrieve the pack’s install path:

import com.google.android.play.core.assetpacks.AssetPackManager;

AssetPackManager mgr = AssetPackManagerFactory.getInstance(context);
AssetPackLocation loc = mgr.getPackLocation("level_data");
if (loc != null) {
    String path = loc.assetsPath();
    // load level files from path
} else {
    // pack not installed yet
    mgr.fetch(Arrays.asList("level_data"));
}

For install-time packs, getPackLocation always returns non-null on a fresh install. For on-demand, it returns null until you call fetch and the download completes.

Testing the AAB

Don’t test via adb install with a single APK extracted from the AAB. The extraction collapses splits and asset packs aren’t correctly represented. Use:

Diagnosing in Play Console

Play Console → App Bundle Explorer → your bundle → Asset Packs tab. Confirm:

If a pack is listed as on-demand when you intended install-time, your gradle file is wrong — fix and re-upload.

Texture Compression Variants

If you ship texture compression variants (ASTC, ETC2), each variant becomes its own pack split delivered to compatible devices. The total bundle can be much larger than what any single device downloads, but make sure each variant’s files cover the same logical content.

Verifying

Install via internal testing on a fresh device. Boot the game. Check the pack’s files exist via adb:

adb shell run-as com.you.game ls files/asset_packs/level_data/

Files should exist immediately after install for install-time packs. If empty, the pack didn’t deliver — revisit delivery_type and testing method.

Understanding the issue

Asset pipelines transform source content into runtime data. Each stage can lose information, change behavior, or introduce platform-specific variations. Bugs at this layer are often invisible until the cooked build runs.

The specific bug described above is the kind that surfaces during integration rather than unit testing. It depends on a combination of factors: the asset configuration, the runtime state, the platform's specific behavior. In isolation, each piece looks correct; in combination, the bug emerges. This is why thorough integration testing - playing the actual game in realistic conditions - catches things that automated tests miss.

Why this happens

This bug class disproportionately affects late-stage development. The work to surface it is interactive testing in realistic conditions, which only really happens after the gameplay is in place and assets are populated. Catching it early requires deliberate testing of conditions that look unimportant.

At the engine level, the behavior comes from a deliberate design decision in the engine. The engine team chose a particular trade-off - usually performance versus convenience, or generality versus specificity - and that trade-off has consequences when you push against it. Understanding the trade-off is what turns 'this bug is mysterious' into 'this bug is the expected consequence of this design'.

Verifying the fix

After applying the fix, the verification step has three parts: confirm the original repro is resolved, confirm no obvious regressions in adjacent functionality, and (for shipping titles) deploy to a small player cohort first and watch the crash and report rates. Each step catches something the others miss.

Reproducibility is the prerequisite for verification. If you can't reliably reproduce the bug pre-fix, you can't reliably verify it post-fix. Spend time getting a clean reproduction before you write any fix code. The fix is fast once you understand the reproduction; the reproduction is the slow part.

Variations to watch for

There's almost always a less obvious case where the same problem applies. The reported case is the one a player hit; the related cases hide because they're rarer or affect fewer players. After fixing the reported case, search the codebase for the pattern - one fix often unlocks several.

Adjacent bugs often share a root cause. After fixing the case you've found, spend an hour searching the codebase for similar patterns. What's the same call with different arguments? The same data flow with a different entity type? The same lifecycle issue in a sibling system? Each match is a candidate for the same fix, or a related fix that prevents future bugs of the same class.

In production

Live games surface this bug class at scale. What's a rare edge case in development becomes a daily occurrence once you have a few thousand concurrent players. The class isn't 'this player has a unique setup'; it's 'one in N thousand sessions will trigger this exact combination'.

When triaging a similar issue in production, prioritize gathering data over hypothesizing causes. A player report describes a symptom; what you need is a build SHA, a session timestamp, and ideally a screen recording or session replay. With those, the bug becomes tractable. Without them, you're guessing at hypothetical reproductions that may not match what the player actually hit.

Performance considerations

If this issue manifests under high load (many actors, many particles, many network connections), profile the post-fix code path with realistic counts. The original cost was a bug; the new cost is real work, and real work has a budget.

Diagnostic approach

Before applying any fix, gather enough context to be confident you're addressing the actual cause and not a similar-looking symptom. The cheapest diagnostic step is reproducing the bug deterministically - if you can't get the same failure twice in a row, your fix attempts will be hard to evaluate. Lock down the reproduction first.

For the engine-specific diagnostics, the editor's profiler is the canonical starting point. Capture a representative frame with the symptom present; compare against a frame without the symptom; the diff often points directly at the cause. If the symptom is non-deterministic, capture multiple frames and look for the pattern - the cause is usually a state transition or a specific input value rather than a continuous effect.

Tooling and ecosystem

Third-party plugins often provide better diagnostics for their own behavior than the engine does. If the affected code is in a plugin, check the plugin's documentation for debug modes, verbose logging, or inspector tools - these can save hours of investigation when they exist.

Within the engine, the relevant diagnostic surfaces include the standard frame debugger, memory profiler, and engine-specific debug overlays. Each one shows a different facet of what's happening. The frame debugger reveals draw call ordering and state transitions; the memory profiler shows allocation patterns; the debug overlay reveals per-system state. Bugs that resist one tool usually surrender to another - the trick is knowing which tool to reach for first.

Edge cases and pitfalls

Platform-specific edge cases are worth enumerating explicitly. iOS handles backgrounding differently than Android; Windows handles focus changes differently than macOS. A fix that works on the development platform may not work on every target. Test on each shipping platform deliberately.

When writing a regression test for this fix, focus on the boundary conditions that surfaced the original bug. Tests that exercise the happy path catch obvious regressions; tests that exercise the boundary catch the subtler regressions that look like new bugs but are really the original returning. The latter are the tests that earn their keep over the long life of the project.

Team communication

Document the fix and its rationale in the commit message or attached engineering doc. Future engineers will encounter related issues; the rationale tells them whether your fix is reusable or specific to the case at hand. Without rationale, the fix gets reverted or copied incorrectly.

If this fix touches a system several engineers work in, a short writeup in the team's engineering channel helps. Not a full design doc - a paragraph explaining what was wrong, what's fixed, and what to watch for. Future engineers encountering similar symptoms will search for the fix; making it findable is a small investment that pays back later.

“AAB asset packs need correct delivery_type in gradle and proper Play install path for testing. Sideloaded APKs lie about pack behavior.”

Always test AABs through Internal App Sharing first — reveals pack delivery issues that adb install hides.