Name
Title | ||
|---|---|---|
Automated Traceability for Domain Modelling Decisions Empowered by Artificial Intelligence |
Abstract | ||
|---|---|---|
Domain modelling abstracts real-world entities and their relationships in the form of class diagrams for a given domain problem space. Modellers often perform domain modelling to reduce the gap between understanding the problem description which expresses requirements in natural language and the concise interpretation of these requirements. However, the manual practice of domain modelling is both ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/RE51729.2021.00023 | 2021 IEEE 29th International Requirements Engineering Conference (RE) |
Keywords | DocType | ISSN |
Domain Models,Traceability,Natural Language (NL),Machine Learning (ML),Traceability Knowledge Graph (TKG),Traceability Information Model (TIM) | Conference | 2332-6441 |
ISBN | Citations | PageRank |
978-1-6654-2856-9 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rijul Saini | 1 | 1 | 3.06 |
| Gunter Mussbacher | 2 | 12 | 9.02 |
| Jin L. C. Guo | 3 | 0 | 0.34 |
| Jörg Kienzle | 4 | 732 | 69.38 |