Going Deeper Than Surface Scans

What Surface-Level Scanning Actually Misses

You need to change a function. So you do what every developer does: you grep for the function name, check the call sites your IDE reports, maybe run a quick search across the project. You find eight callers. You update them. You push. And then something breaks in staging that has nothing to do with any of those eight files.

What happened? The function was also called dynamically through a string-based lookup in a plugin loader. It was referenced indirectly through a middleware chain that wraps handlers at startup. A test helper constructed a mock that delegates to it at runtime. None of these show up in a text search. None of them appear in your IDE's "find all references" panel.

This is the gap between surface scanning and deep code analysis. Surface tools answer the question "where does this string appear?" Deep analysis answers a fundamentally different question: "what actually happens when this code runs?"

That distinction matters because modern codebases are not flat collections of files. They are layered systems with dependency injection, event-driven architectures, middleware pipelines, dynamic imports, and reflection. The relationships that matter most are often the ones that no static text search can find. LOOM's code scanner is built specifically for this kind of depth.

Three Dimensions of Deep Code Analysis

Surface tools see text. LOOM sees behavior, flow, and structure.

Execution Path Tracing

Consider a real scenario. You have a validation function called validateEmail. Your IDE's "find references" shows five direct callers: two form handlers, an API endpoint, a test file, and a utility wrapper. Five callers. Straightforward change, right?

LOOM traces the execution path and finds twenty-three. The utility wrapper is called by a middleware chain that applies it to every incoming request matching a route pattern. That middleware is registered dynamically at startup by a plugin system. Three microservices import that plugin. Each of those services has its own set of callers that eventually reach your validation function through two, three, sometimes four layers of indirection.

Five callers versus twenty-three. That is the difference between execution path tracing and text search. LOOM does not just find where a symbol appears in your source files. It follows the call chain -- through conditionals, through callbacks, through promise chains, through event emitters -- and shows you every path that reaches the code you are about to change.

This is what dependency tracing actually means: not a list of files, but a graph of execution paths. You can explore it visually in the dependency mapper or drill into individual call chains. Either way, you see the blast radius before you commit.

Data Flow Mapping

A user submits a form. The input hits a controller, gets validated, gets normalized, gets passed to a service layer, gets transformed into a database model, gets persisted. Along the way, that data changes shape at least four times. Fields get renamed. Types get coerced. Nested objects get flattened or expanded. Optional fields get defaults.

Now ask yourself: if you change the shape of that form input, where does it break? Not just the controller that receives it directly -- what about the service layer that expects a specific field name? What about the mapper that assumes a nested structure? What about the downstream event that broadcasts the persisted entity with fields derived from the original input?

Data flow mapping traces these transformations across your entire system. LOOM shows you not just where data enters and exits, but every point where it changes shape. You can follow a single field from the HTTP request body through every function that touches it, all the way to the database column where it lands. If your change breaks a transformation three layers deep, you will see it before you ship it.

Hidden Dependency Discovery

Some dependencies are obvious. You import a module, you call a function, the relationship is right there in the source. But the dependencies that cause the most damage are the ones you cannot see by reading import statements.

Runtime dependency injection: your service gets its database adapter injected at startup by a container that reads a config file. There is no import between the service and the adapter. They are coupled at runtime, invisible at read time. Dynamic requires: a plugin loader reads a directory, iterates over filenames, and requires each one. The dependency between the loader and the plugins exists only at execution time. Reflection-based calls: a framework dispatches method calls based on string matching against route patterns. The connection between the route definition and the handler function is never declared as an import.

These patterns are everywhere in modern codebases. Dependency injection containers, event bus architectures, decorator-based routing, factory patterns with dynamic class loading. Each one creates connections that traditional static analysis cannot follow. LOOM detects these hidden dependencies by analyzing not just what your code says, but what it does -- how objects are constructed, how functions are dispatched, and how modules are wired together at runtime.

What Surface Scanning Misses

A side-by-side comparison of what text-based search and IDE tools catch versus what deep code analysis reveals.

To understand how LOOM's approach differs from traditional lint-and-flag tools in more detail, see LOOM vs Static Analysis.

Deep Does Not Mean Slow

The usual tradeoff with deep code analysis is performance. Deeper analysis means longer scan times, heavier memory usage, more waiting. LOOM breaks that tradeoff. The AST-based scanning engine is built to handle large codebases without requiring overnight batch runs or dedicated build servers. You get execution path tracing and dependency discovery at speeds that keep up with your development workflow, not slow it down. For specifics on how this works at scale, see the performance page.

Going Deeper

Analysis depth is one part of the LOOM platform. If you want to understand the broader concept of code intelligence and how it differs from traditional tooling, start with our code intelligence guide. For a practical walkthrough of dependency mapping -- how to read a dependency graph, what the clusters mean, and how to use that information for refactoring decisions -- see the dependency mapping guide.