GitHub Actions assembly line configuration maintenance method

By combining a layered knowledge base and a large language model with abstract syntax tree technology, we can automatically locate and fix configuration errors in the GitHub Actions pipeline, detect and optimize configuration smells, solve the problem of aborts caused by configuration issues in existing technologies, and improve development efficiency and quality.

CN121934880APending Publication Date: 2026-04-28NANJING UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
NANJING UNIV
Filing Date
2026-01-22
Publication Date
2026-04-28

AI Technical Summary

Technical Problem

In existing technologies, configuration errors and configuration smells in the GitHub Actions pipeline can cause execution to stop, and there is a lack of efficient detection and repair tools, resulting in low development efficiency and high costs.

Method used

It adopts a hierarchical knowledge base architecture and a large language model, combined with abstract syntax trees and regular expression matching technology, to realize the automatic location and repair of configuration errors, and to perform automated detection and optimization by building an odor template list.

Benefits of technology

It has enabled automated pipeline configuration and maintenance, reducing the time cost of manually fixing configuration errors and optimizing configuration quirks, and improving software development efficiency and the quality of continuous integration.

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Abstract

The invention discloses a GitHub Actions assembly line configuration maintenance method, belongs to the technical field of software engineering and continuous integration, is used for solving the problems of GitHub Actions assembly line configuration error repair and configuration peculiar smell optimization, and overcomes the defects of low configuration error positioning efficiency, lack of repair schemes and insufficient configuration peculiar smell detection tools in the prior art. The method comprises the following steps: constructing a hierarchical knowledge base framework, integrating community discussion, historical repair records and a general error template, and realizing knowledge retrieval from fine granularity to coarse granularity; a large language model and a retrieval enhancement generation technology are combined, and accurate positioning and repairing of configuration errors are completed; an odor template list is constructed and configured based on three dimensions of safety, performance and specification, and automatic detection and optimization of odor are realized through an abstract syntax tree and a regular matching technology. According to the method, the repair efficiency of configuration errors can be remarkably improved, the execution efficiency and maintainability of the assembly line are optimized, and a systematic solution is provided for quality guarantee of continuous integration of the assembly line.
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