Code change statistical method and system considering different editing scenes

By listening to events in different editing scenarios and uploading multi-dimensional data, the problem of coarse granularity and incomplete data in code change statistics in the VSCode environment has been solved. This has enabled efficient multi-dimensional data display and accurate code change statistics, improving data utilization and project management efficiency.

CN120973846AInactive Publication Date: 2025-11-18BEIJING YIYUANKU TECH CO LTD
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Patent Information

Application Number
CN202511501277.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-10-21
Publication Date
2025-11-18
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

Existing technologies in the VSCode environment have coarse-grained code change statistics methods, cannot distinguish between different editing scenarios, have incomplete data collection, lack multi-dimensional display, and cannot meet the needs of project management and developers.

Method used

By listening to events in different editing scenarios, including copy and paste, repeated changes, AI adoption, and AI completion, and combining them with save events, editor exit, and scheduled tasks, multi-dimensional data can be uploaded and visualized.

Benefits of technology

It improves the accuracy and data utilization of code change statistics, reduces errors, ensures data integrity, provides multi-dimensional visualization, and supports project management and developer analysis.

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Abstract

The invention discloses a code change statistical method and system considering different editing scenes, and relates to the technical field of computer software development and code management, and the method comprises the following steps: S1, obtaining code change data under different editing scenes through an event monitoring mode; wherein the different editing scenes comprise copying and pasting, repeated changing, AI (Artificial Intelligence) adopting and AI complementing; s2, uploading the code change data based on three dimensions of a saving event, an editor exit and a timed task; and S3, performing statistics of different dimensions and visual display of different forms on the uploaded code change data. According to the method, a reliable data basis is provided for code quality evaluation and developer performance analysis.
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