I am trying to record the requests and responses in a WKWebView, but instruments does not seem to record them. Is this to be expected? The webView is set to inspectable, and I am using the HTTP Traffic instrument. All the requests the app is doing are recorded, but neither the original request for the webView, nor subsequent traffic is recorded. When I use Safari to inspect the webView, all I see is the last page (even when I start the inspector before the first request is made). How can I see these requests?

I made a box with MDLMesh.newBox(). I added normals. let mdlMesh = MDLMesh.newBox(withDimensions: SIMD3<Float>(1, 1, 1), segments: SIMD3<UInt32>(2, 2, 2), geometryType: MDLGeometryType.triangles, inwardNormals:false, allocator: allocator) mdlMesh.addNormals(withAttributeNamed: MDLVertexAttributeNormal, creaseThreshold: 0.25) After I convert to MTKMesh the normals are (0,0,0) for a group of vertices. I can only inspect the geometry after I convert to MTKMesh. Is there a way you can use Geometry Viewer on a MDLMesh?

hello, I got a question about coreml. I loaded the coreml model in the project and set the computing unit to CPU+GPU. When I used instruments to analyze the performance, I found that there was an overhead of prepare gpu request before each inference. I also checked the freezing point graph and found that memory was frequently allocated. Is this as expected? Is there any way to avoid frequent prepares? I have tried some methods, such as memory sharing of predict interface input parameters, but it seems to be ineffective.

I could use the Metal System Trace before the most recent update, but now whenever I try to profile using the Metal Counter instrument, I get the [Warning] GPU Service reported error: Selected counter profile is not supported on target device. What is the issue here?

It seems as though using any initializer of SubscriptionOfferView or StoreView will create a memory leak. This can be simply reproduced by adding this to your SwiftUI view: SubscriptionOfferView(groupID: "yourgroupID", visibleRelationship: .all, useAppIcon: true) or StoreView(ids: ["monthly", "yearly"]) Tested on iOS 26 beta 2

Dear Apple Developer Support team, I would like to request an official confirmation regarding the handling of transaction status in the App Store Server API, specifically for the GET /inApps/v1/transactions/{transactionId} endpoint. As per our current understanding from the official documentation (Get Transaction Info), the API’s behavior appears to be: If a transaction is finalized and successfully processed by App Store, querying this API will return HTTP 200 OK along with transaction details. If a transaction is still in a pending or deferred state (such as awaiting Ask to Buy approval or pending authorization), the API will not return a 200, and instead respond with HTTP 404 Not Found or an appropriate error. Could you please confirm if this behavior is accurate and officially supported? Specifically: Does a 200 OK response guarantee that a transaction is finalized and successfully recorded on App Store servers? In cases where a transaction is pending approval (e.g. Ask to Buy), is it correct that GET /transactions/{transactionId} would return 404 Not Found until the transaction is finalized? We would greatly appreciate your confirmation to align our server-side logic for transaction validation accordingly. Thank you very much for your support! Kind regards, cuongnx

Hello We use Datadog Mobile Vitals in our app and I'm trying to run some tools in Instruments for comparison. I'm not sure what tool should I use for some of those metrics: Slow Renders Description: With slow renders data, you can monitor which views are taking longer than 16ms or 60Hz to render. Instruments equivalent: Hangs including microhangs (?) CPU ticks per second Description: RUM tracks CPU ticks per second for each view and the CPU utilization over the course of a session. The recommended range is <40 for good and <80 for moderate. Instruments equivalent: CPU Profiler (?) Frozen Frames - Description: Frames that take longer than 700ms to render appear as stuck and unresponsive in your application. These are classified as frozen frames. Instruments equivalent: Hangs with > 500ms (?) Memory Utilization Description: The amount of physical memory used by your application in bytes for each view, over the course of a session. The recommended range is <200MB for good and <400MB for moderate. Instruments equivalent: Allocation (?)

I tried using Create ML of Xcode 26.0 beta 7 to generate a model using the "Word Tagging" template, and I received the error: Training progress unavailable - Unexpected error. Using Create ML of XCode 16.4 with the same documentation, I was able to build the model and use it in a test app. I'd like to understand why Create ML of Xcode 26 no longer works.

Hello Apple team, I am using xctrace to record an Allocations trace on iOS. For example: xctrace record --template "Allocations" --launch com.example.myapp --time-limit 30s --output alloc.trace After recording, I can export the results in Allocations List format (flat list of allocations) using: xcrun xctrace export --input ./alloc.trace --xpath '/trace-toc/run/tracks/track[@name="Allocations"]/details/detail[@name="Allocations List"]' --output ./alloc.xml This works fine and produces an XML output. However, what I really need is to export the data in Call Tree format (as shown in Instruments GUI). I checked xctrace export --help, but it seems that the Allocations template only supports the List view for export, not the Call Tree breakdown. My question is: 👉 Is there a way to export an Allocations trace in XML with Call Tree details using xctrace? 👉 If not, is there an API or recommended workflow to automate this instead of exporting manually from Instruments GUI? Thanks in advance for your help!

I'm working on a custom instrument that displays intervals from os_signpost data. I'd like to color the intervals in the graph based on data from an accompanying aggregate. For example, color the interval red if its duration is greater than 3 standard deviations from the mean. Is this possible?

I have been working on battery consumption analysis for my application, and as part of this effort, I wanted to understand how competitor apps behave under similar usage conditions. To do this, I downloaded competitor apps from the App Store and attached them to Instruments via Xcode. I then executed a defined set of manual test scenarios to simulate real user behavior. During these tests, the iPhone was connected to a Mac and charging continuously, which meant that System Power Usage logs were not generated in Instruments. However, I was able to capture detailed metrics such as: Network usage CPU load GPU activity Display and brightness impact Other runtime performance characteristics Since direct battery drain data was unavailable, I used derived analysis (with AI assistance) to estimate approximate power consumption based on the above metrics, assuming real-device (battery-powered) conditions. According to Apple documentation, System Power Usage in Instruments is not directly tied to the device’s battery percentage. Instead, it appears to be computed using contributing factors such as CPU, network, display, and other subsystem activity. This raises a few important questions about data reliability and methodology. Key questions: How reliable are Instruments-based metrics (CPU, network, display, GPU) for estimating real-world battery consumption when the device is charging? Can these metrics be safely used as a comparative baseline between competitor applications, even if absolute battery drain values are unavailable? Is the System Power Usage instrument essentially a derived model based on subsystem activity, and if so, does it remain accurate when the device is not discharging? From Apple’s perspective, is this a valid approach for relative power comparison, provided that: The same device is used OS version is identical Test scenarios are consistent and repeatable Based on these findings, would it be reasonable to proceed with instrumenting our own application, run the same scenarios, and draw conclusions using relative comparisons rather than absolute battery percentages? The intent is not to claim exact battery drain numbers, but to establish a directionally correct and repeatable comparison that can guide performance optimizations in our own application. I would like to understand whether this methodology aligns with Apple’s recommended practices, or if there are limitations or inaccuracies I should be aware of before relying on these results for decision-making.