Name
Abstract | ||
|---|---|---|
In the software engineering process management, the level of regulation standardization and the depth of execution are one of the major marks of software management. Reducing human cost out of process training and compliance audit and improving the effectiveness of system management have attracted more and more attention to financial enterprises. Through the semantic markup platform and Neo4j graph database technologies, we are to develop the regulation knowledge graph which is appropriate for software waterfall model development and management. The regulation knowledge graph displays intuitive and comprehensive of the whole life cycle of software development in all kinds of specification information. It also improves software development process specifications and corresponding information query efficiency, accuracy and integrity. The regulation knowledge graph can rapidly and continuously integrate regulation knowledge information, significantly improve the efficiency of acquiring, sharing and maintaining regulation knowledge, reduce software labour costs and enhance the ability of enterprises to analyze and apply regulation information and data, which has wide application value in the construction of internal control management of enterprises. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/QRS-C51114.2020.00077 | 2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C) |
Keywords | DocType | ISBN |
Knowledge graph,Regulation management,Graph database,Project management | Conference | 978-1-7281-8916-1 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yingying Wang | 1 | 21 | 11.64 |
| Jun Zhao | 2 | 0 | 0.34 |
| Feng Li | 3 | 0 | 0.34 |
| Min Yu | 4 | 0 | 0.34 |