Component memory leak detection method and device, computer equipment and storage medium

By generating heap snapshots and structured operation logs, memory leaks in Vue.js 2 components are automatically identified, solving the problem of inefficiency in existing tools and achieving efficient and accurate memory leak detection and repair.

CN121682182APending Publication Date: 2026-03-17ZHEJIANG BANGSUN TECH CO LTD
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Patent Information

Application Number
CN202511797247.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-01
Publication Date
2026-03-17

AI Technical Summary

Technical Problem

Existing memory leak detection tools cannot effectively identify hidden memory leaks in Vue.js 2 components and rely on manual operation, resulting in low detection efficiency and insufficient accuracy.

Method used

By generating heap snapshots of component creation and destruction events, combined with structured operation logs and information mapping tables, the system automatically identifies component instance nodes, constructs a memory reference graph, performs reverse breadth-first search and path compression, determines the root cause of memory leaks, and generates a visual report.

Benefits of technology

It enables automated detection of memory leaks in Vue.js 2 components, improving detection efficiency and accuracy, reducing the cost of detection and repair for developers, and accurately locating memory leak issues.

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Abstract

The invention relates to a component memory leak detection method and device, computer equipment and a storage medium. The method comprises the following steps: when a component creation event of a to-be-detected component is detected, generating a first heap snapshot corresponding to the component creation event of the to-be-detected component, and when a component destruction event of the to-be-detected component is detected, generating a second heap snapshot corresponding to the component destruction event of the to-be-detected component; analyzing the first heap snapshot and the second heap snapshot, and determining target structured data; component instance nodes are determined, and an information mapping table between node unique identifiers and node information of the component instance nodes is constructed; determining candidate memory leak components according to the information mapping table; and determining a target memory leak component from the candidate memory leak components according to the component-created document object model and the component-destroyed document object model of the candidate memory leak components. And the identification precision of the component memory leak scene is improved.
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