🤖 AI Summary
This work addresses the challenge of simultaneously preserving visual layout, editable structure, and semantic consistency when editing software diagrams via natural language. The authors propose a structured agent-based diagram editing paradigm that models diagrams as editable graph structures, parses natural language instructions into structured editing intents, and translates these into sequences of graph operations. This approach enables semantics-preserving editing for both Draw.io and Mermaid formats by integrating state management with model-driven interpretation, and combines direct canvas manipulation with masked image editing to support recoverable, versioned workflows. Evaluation on a Kubernetes architecture case study demonstrates advantages in structural validity, editing success rate, and preservation of irrelevant elements, while unit tests provide insight into failure modes.
📝 Abstract
Software diagrams are difficult to edit through human-friendly interfaces because edits expressed in natural language must still preserve visual layout, editable structure, and semantic relationships. As a step forward, we present SAGE, a browser-based tool for prompt-guided editing of Draw.io and Mermaid-style engineering diagrams. The tool maps diagrams into an editable graph representation, translates natural language requests into structured edit intents, analyzes those intents into graph-oriented operation steps, validates and repairs common Draw.io XML issues, and stores successful results as recoverable versioned artifacts. This design separates structured state management from model-driven interpretation, while acknowledging that some prompt-guided XML edits remain model-assisted. The tool also supports direct canvas editing and a secondary mask-based image-editing workflow. We evaluate the system using unit tests and a Kubernetes architecture case study, measuring structural validity, edit success, preservation of unrelated elements, and failure causes.