Open source platform task matching method based on developer behavior graph and double-layer filtering

By using developer behavior mapping and a two-layer filtering method, developers on open-source platforms are accurately matched, solving the problem of inaccurate developer matching in existing technologies, improving task completion quality and efficiency, and reducing management costs.

CN121658534BActive Publication Date: 2026-06-26NAT INNOVATION INST OF DEFENSE TECH PLA ACAD OF MILITARY SCI
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Patent Information

Application Number
CN202610176226.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-02-06
Publication Date
2026-06-26
Estimated Expiration
2046-02-06

AI Technical Summary

Technical Problem

Existing technologies cannot efficiently and accurately match the most suitable developers to complete tasks on open source platforms, resulting in inconsistent task completion levels and high management costs.

Method used

By using a developer behavior graph and a two-layer filtering method, developer behavior features are divided into three dimensions: basic skills, collaborative relationships, and historical behavior. A knowledge graph is constructed, and task requirements are decomposed and vectorized using natural language processing technology. Combined with collaborative ability and reliability scores, a two-layer filtering method is used to generate recommendation results.

Benefits of technology

It enables efficient and accurate matching of developers on open-source platforms, improving the quality and efficiency of task completion and reducing management costs.

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Abstract

The application discloses an open source platform task matching method based on a developer behavior graph and double-layer filtering, and the method comprises the following steps: S1, dividing the behavior characteristics of the developer into three dimensions to obtain a developer behavior characteristic data set; S2, identifying and screening out developers who do not meet the basic requirements; S3, constructing a lightweight knowledge graph G containing the nodes of the developers, the nodes of the tasks and the associated edges of the nodes by combining a relational database with a graph storage dev_task ; S4, obtaining the task requirement text Text req of the historical open source task and the open source task to be matched, obtaining a plurality of subtasks by decomposing the requirement of the open source task to be matched, and generating corresponding vectorized representations; S5, adopting a double-layer filtering strategy, constructing a primary matching layer to obtain a preliminary candidate developer set, and constructing a fine screening scoring layer to obtain a recommended result list.
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