Software Test Data Management Based on Knowledge Graph

Abstract

With the maturity of software development models and methods, large-scale software is born. However, the current problem is the lack of a software test data management model that integrates basic data management and advanced knowledge reasoning. To solve this problem, we constructed a software test data management model based on knowledge graph, so as to realize the intelligent management and reasoning of software test data. The model adopts the entity extraction model based on feed-forward neural network, the knowledge graph integration method based on graph database, and the knowledge reasoning submodule based on deep learning. In order to validate the model effect, we evaluated the functions of the three modules separately. Specifically, the knowledge graph construction accuracy, knowledge query response time, and knowledge inference accuracy are higher than the currently existing methods.

Authors

  • Li Gao Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huai’an 223003, China
  • Junlin Qiu Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huai’an 223003, China
  • Guanhua Chen Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huai’an 223003, China

DOI:

https://doi.org/10.31449/inf.v48i16.6416

Downloads

Published

10/02/2024

How to Cite

Gao, L., Qiu, J., & Chen, G. (2024). Software Test Data Management Based on Knowledge Graph. Informatica, 48(16). https://doi.org/10.31449/inf.v48i16.6416

Issue

Vol. 48 No. 16 (2024): Online-only issue

Section

Online-only

License

Authors retain copyright in their work. By submitting to and publishing with Informatica, authors grant the publisher (Slovene Society Informatika) the non-exclusive right to publish, reproduce, and distribute the article and to identify itself as the original publisher.

All articles are published under the Creative Commons Attribution license CC BY 3.0. Under this license, others may share and adapt the work for any purpose, provided appropriate credit is given and changes (if any) are indicated.

Authors may deposit and share the submitted version, accepted manuscript, and published version, provided the original publication in Informatica is properly cited.