Software quality detection method and device, electronic equipment and medium

By constructing a software knowledge graph for static and dynamic analysis, the problem of insufficient test coverage and high cost caused by relying on human experience in existing technologies is solved, achieving efficient and accurate software quality testing and adapting to the high-frequency iteration needs of the financial industry.

CN121979778APending Publication Date: 2026-05-05CHINA CONSTRUCTION BANK
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Patent Information

Application Number
CN202511936546.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-22
Publication Date
2026-05-05

AI Technical Summary

Technical Problem

In existing technologies, software quality inspection relies on human experience, resulting in insufficient test coverage, low inspection efficiency, high maintenance costs for quality inspection rules, and an inability to adapt to high-frequency iteration requirements.

Method used

By constructing a software knowledge graph, integrating code data, runtime data, and historical quality-related data, static and dynamic analysis is performed to generate a quality analysis report.

Benefits of technology

It achieves comprehensive coverage of code logic, business scenarios, and abnormal branches, improving the accuracy and efficiency of quality inspection and adapting to the application scenarios of large-scale centralized production and high-frequency iteration in the financial industry.

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Abstract

The embodiment of the invention discloses a software quality detection method and device, electronic equipment and a medium, and relates to the technical field of computers.The specific implementation mode of the method comprises the steps that code data, operation data, historical test defect data and historical operation alarm data of software to be detected are obtained; based on the code data, the operation data, the historical test defect data and the historical operation alarm data, constructing a software knowledge graph reflecting an association relationship between internal entities of the to-be-tested software; and based on the software knowledge graph, performing quality analysis on the to-be-tested software to obtain a quality analysis result, and generating a quality analysis report according to the quality analysis result. The method can effectively reduce the human input and the detection period, improves the accuracy and efficiency of software quality detection by means of the deep correlation analysis capability of the knowledge graph, and meets the application scene requirements of large-scale centralized production and high-frequency iteration in the financial industry.
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