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Technical Foundations

Code Scalpel is built on deterministic representations of code.

That matters because AI models are good at language generation, but software engineering requires more than language generation. It requires structure, relationships, and bounded reasoning.

ASTs: The Structural Base

The first layer is the Abstract Syntax Tree, or AST.

An AST is the parsed structural form of the code. It tells the system what functions, classes, methods, imports, and control structures actually exist.

That makes ASTs the right foundation for tasks such as:

  • precise code extraction
  • code structure analysis
  • exact symbol discovery
  • lightweight file summaries

IR: The Shared Semantic Layer

Different languages expose different AST shapes.

Code Scalpel uses a normalization layer to map those language-specific structures into a shared intermediate representation, or IR, where appropriate. That makes it possible to reuse higher-level reasoning logic across multiple languages without pretending that all languages are identical at the parser level.

Graphs: The Relationship Layer

Once code is represented structurally, the next important question is how parts of the system relate to each other.

That is where graph-oriented analysis becomes useful.

Examples include:

  • call graphs
  • dependency graphs
  • neighborhood graphs
  • project maps

These representations help answer questions about impact, architecture, and blast radius.

Symbolic And Security Workflows

The next layer is bounded reasoning about paths and data movement.

This is where capabilities such as symbolic execution, test generation, and security-oriented source-to-sink analysis become important.

These workflows move beyond syntax into higher-value engineering questions such as:

  • what execution paths exist
  • what test cases are needed
  • where tainted data can flow
  • what risks exist across files or modules

How The Layers Fit Together

flowchart TB
    A[Source code] --> B[Parsers]
    B --> C[AST layer]
    C --> D[IR normalization]
    D --> E[Graph and dependency analysis]
    D --> F[Symbolic and security analysis]
    E --> G[Tool interfaces]
    F --> G

Why This Matters

The important point is not just that Code Scalpel can parse code. Many tools can do that.

The important point is that Code Scalpel turns parsed structure into reusable, deterministic tool operations that AI assistants and other workflows can use directly.

That is why the system is useful as a code-intelligence layer rather than only as a code-reading utility.