AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Uncer-tainTree is presented, an end-to-end process that makes uncertainty a first-class concern of multi-model consistency analysis and includes interacting uncertainties from various sources and models.
Context: Technically sophisticated systems are the result of the joint work of several domain experts. However, the more people collaborate, the more important it becomes to make the model evolution and its single edit operations accessible and comprehensible for involved stakeholders. Objective: We developed the textual and semantic-aware query language EditQL. It enables domain experts to search for model versions, changes, and causing edit operations within a model’s edit history. Based on an operation-based versioning system, the query language covers both edit operations and all model states. Method : We systematically elaborate the requirements of a query language for edit histories. Based on this, we present a DSL integrated into an existing modeling tool. We conducted a mixed-methods usability study with 15 participants in which they had to answer various questions about a model’s evolution using EditQL. Results: All participants agreed on the usefulness of the query language, particularly the possibility of querying for semantic changes in the model. The measured System Usability Scale (SUS) scores range from OK to good . In addition, we identified a set of possible improvements. Conclusion: The study confirmed that EditQL and the underlying concepts are suitable tools to help domain experts understand the evolution of a model.
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