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# Beyond UML: Making Sense of AI-Generated Code through Visual Architecture

Stop reviewing AI-generated code with linear diffs. Discover how a visual architecture plugin instantly exposes lazy AI shortcuts and validates system-wide structural integrity before you merge.

- **Speakers:** [Sven Reinck](https://www.wearedevelopers.com/@sven-reinck)
- **Event:** World Congress 2026 Europe - Virtual Stage
- **Published:** July 3, 2026
- **Duration:** 26:02
- **URL:** https://www.wearedevelopers.com/videos/2102-beyond-uml-making-sense-of-ai-generated-code-through-visual-architecture

## Summary

As AI coding assistants increasingly transform software development, reviewing the architectural impact of AI-generated code has become a significant bottleneck. When a single prompt alters dozens of tightly coupled files, reading linear code diffs obscures broader structural changes. Drawing a parallel to the invention of the disassembler—which allowed skeptical developers to verify early assembly compilers until trust was built—this presentation introduces a visual architecture plugin for IntelliJ designed to act as a "disassembler" for AI modifications. By providing a graph-based representation alongside project files, developers can rapidly assess architectural shifts before diving into individual methods.

The presentation demonstrates applying this structural lens to refactor a 20-year-old legacy compiler codebase, systematically decoupling tangled package dependencies and extracting domain logic from utility elements. Because large language models frequently excel at writing isolated methods but struggle with system-wide coherence, verifying the structural integrity of AI suggestions is crucial. Visualizing the dependency graph makes it easier to spot cyclical dependencies, validate architectural boundaries, and safely plot refactoring paths for legacy models.

A notable highlight exposes a common pitfall in AI-assisted development: AI "laziness." When tasked with implementing a visitor pattern to safely separate logic from data structures, the AI took a shortcut by embedding the new visitor call inside an obsolete update method rather than fully replacing the references. The visualization tool instantly exposed this lingering structural flaw, enabling the developer to cleanly resolve it using native IDE conventions. Ultimately, integrating structural graphing into AI coding workflows empowers developers to prioritize architectural soundness and quickly validate the bigger picture.

**Keywords:** ai code review, visual software architecture, intellij visualization plugin, legacy codebase refactoring, dependency graph visualization, visitor pattern implementation, coding assistant structural impact, ai refactoring laziness, decoupling structural dependencies, architectural boundary validation, ai code generation verification, software cycle detection

## Chapters

1. **Challenges of reviewing AI-generated code changes** (00:02) — Navigating multiple files modified by AI prompts creates a significant manual review burden.
1. **Proposing visual solutions for navigating code changes** (01:42) — Visualizing file interactions creates a faster overview for prioritizing manual code evaluation.
1. **Drawing parallels between early compilers and AI tools** (03:01) — Reconstructing code structure builds trust in AI generation similarly to how disassemblers validated early compilers.
1. **Visualizing AI refactoring impact with architecture graphs** (04:31) — Comparing program states through a graph view isolates boilerplate patterns from actual logic changes.
1. **Untangling logical coupling in legacy codebases** (09:25) — Graph-based visualization guides the gradual decoupling of cyclical dependencies and misguided package structures.
1. **Resolving structural domain leaks in utility packages** (15:52) — Removing incoming domain object dependencies from exception classes via automated factory methods restores pure utility status.
1. **Spotting incomplete structural migrations during AI refactoring** (19:05) — Visualizing architecture exposes lazy AI implementations that wrap legacy methods instead of fully restructuring object references.

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