Apple Inc.

Phillip J Azar

Ashish Patro

Amit K Vyas

Abhinav Pathak

Conor J O'Reilly

Anthony R Newnam

Anshul Dawra

Kaiehu Kaahaaina

Prajakta S Karandikar

Soren C Spies

Andreas C Bergen

Daniel J Etter

Sheyas Binnamangala Prabhu

Akhil Bhutani

Komlan J Attiogbe

Shefali Saboo

Nehal Bhandari

Jeffrey S Lale

Andrew P Sakai

Terrence R Long

Conor J O'REILLY

A server computer may receive diagnostic logs from a plurality of electronic devices, each having a particular application installed. The diagnostic logs include information about call stacks within the particular application on the plurality of electronic devices for writing data to a storage device that is on each of the plurality of electronic devices. The server computer may filter out a set of call-paths from the call stacks to obtain a set of functions that can be used identify a set of call-path signatures. The server computer may for each function, determine a cumulative measure of write operations to the storage device performed by the function across the electronic devices and identify at least one function from the set of call-path signatures matching one or more predetermined criteria associated with the cumulative measures. The server computer may provide a notification and a corrective action to a developer.

Techniques and devices for qualitative disk writes performance anti-pattern detection and mitigation

Embodiments of the present disclosure present devices, methods, and computer readable medium for techniques for measuring operational performance metrics, and presenting these metrics through an application programming interface (API) for developers to access for optimizing their applications. Exemplary metrics can include central processing unit or graphics processing unit time, foreground/background time, networking bytes (per application), location activity, display average picture luminance, cellular networking condition, peak memory, number of logical writes, launch and resume time, frame rates, and hang time. Regional markers can also be used to measure specific metrics for in application tasks. The techniques provide multiple user interfaces to help developers recognize the important metrics to optimize the performance of their applications. The data can be normalized over various different devices having different battery size, screen size, and processing requirements. The user interfaces can provide an intelligent method for visualizing performance changes for significant changes in application versions.

Logging techniques for third party application data

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Oakland, CA, United States of America

Phillip J Azar