Diagnostic method, apparatus, electronic device, and storage medium

By acquiring and aggregating the call stack and performance metrics of the target application, and using similarity strategies and threshold comparisons, the system automatically divides abnormal and normal call stack sets, and combines function frequency to attribute faults. This solves the problem of low fault diagnosis efficiency in existing technologies and achieves efficient and automatic performance problem localization.

CN122285473APending Publication Date: 2026-06-26NETEASE MEDIA TECH BEIJING
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-17
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

Existing technologies struggle to automatically and accurately pinpoint application performance issues to specific code logic or business scenarios that cause performance degradation, leading to a heavy reliance on human experience and post-event analysis for troubleshooting, resulting in low efficiency.

Method used

By acquiring multiple target call stacks and their performance metrics during the runtime of the target application, the similarity between target call stacks is determined using a preset similarity determination strategy. Based on the similarity, the call stacks are aggregated to form call stack clusters. By comparing the performance metrics within the clusters with preset thresholds, abnormal and normal call stack sets are automatically divided, and fault attribution is performed based on the frequency of function occurrence.

Benefits of technology

It achieves an automated, data-driven diagnostic loop from problem symptoms to code root causes, significantly improving the efficiency and accuracy of problem diagnosis, automatically locating key code functions, and reducing reliance on manual investigation.

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Abstract

This application discloses an application diagnostic method, apparatus, electronic device, and storage medium, relating to the field of computer technology. The method includes: acquiring multiple target call stacks and corresponding performance metrics of a target application during runtime; determining the target similarity between any two target call stacks; aggregating the multiple target call stacks based on each target similarity to obtain at least one call stack cluster; for any call stack cluster, comparing the performance metrics of the call stack cluster with a first preset threshold to obtain a first call stack set and a second call stack set corresponding to the call stack cluster, where the first call stack set represents abnormal call stack performance and the second call stack set represents normal call stack performance; and using the function occurrence frequency of the first and second call stack sets to attribute faults in the target application and obtain the attribution result. By implementing the technical solution of this application, the root cause of application performance problems can be automatically located, improving diagnostic efficiency.
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