A method for building a knowledge graph-based mobile application function point test knowledge base
By constructing a knowledge graph-based mobile application function point testing knowledge base and utilizing OCR, CNN, and coreference resolution techniques, the problem of redundant human resources in mobile application testing tools is solved, achieving efficient and automated mobile application testing.
CN117194213BActive Publication Date: 2026-07-24NANJING UNIV
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Patent Information
- Application Number
- CN202210700870.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-05-27
- Publication Date
- 2026-07-24
- Estimated Expiration
- 2042-05-27
AI Technical Summary
Technical Problem
Existing mobile application testing tools suffer from redundant manpower costs, requiring test scripts to be rewritten for different versions and minor layout changes, resulting in low testing efficiency.
Method used
By constructing a knowledge graph-based mobile application function point testing knowledge base, and utilizing OCR, CNN, Canny algorithms, and coreference resolution technology, features and relationships are extracted from crowdsourced test reports to generate an automated test knowledge base, enabling efficient testing of mobile applications.
Benefits of technology
It enables knowledge base updates within 3 seconds, improving the automation of testing, reducing labor costs, and is suitable for testing mobile applications of different versions and layouts.
✦ Generated by Eureka AI based on patent content.
Abstract
A method for building a knowledge graph-based mobile application function point test knowledge base, comprising a knowledge graph feature extraction module, a knowledge graph relationship extraction module, a knowledge graph co-reference resolution module and a knowledge graph intelligent query module. The knowledge graph feature extraction module is mainly responsible for effectively extracting and disassembling a large number of mobile application crowd testing reports with repeated steps to help build a knowledge graph. The knowledge graph relationship extraction module is to integrate the data analyzed in the knowledge graph feature extraction module. The knowledge graph co-reference resolution module is to upload the data sorted by the knowledge graph feature extraction module and the knowledge graph relationship extraction module to the designated Neo4j graph database. The knowledge graph intelligent query module is to help users quickly understand the knowledge graph and provide guidance for mobile application automated testers on what to do next in automated function point testing.
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