The Foggy Function Detector: A Lightweight Ritual for Spotting Confusing Code Before It Spreads

Every mature engineering team has the same story: a piece of code that “worked fine” when it was written, quietly became central to the system, and years later everyone is afraid to touch it. The function is technically correct, but nobody really understands it without a 30‑minute guided tour.

That early discomfort you felt reading it? That was your warning.

In this post, we’ll name that feeling, turn it into a clear signal, and define a lightweight ritual you can apply in minutes: The Foggy Function Detector. The goal: catch confusing code early, before it quietly spreads and hardens into technical debt.

What Is a “Foggy Function”?

A foggy function is a code smell: the code runs, tests are green, but something about it feels unclear, fragile, or harder to reason about than it should be.

Typical signs you’re looking at a foggy function:

You have to keep re-reading lines to understand what’s going on.
You mentally trace the logic several times before you feel confident.
You’d struggle to summarize the function’s purpose in one short sentence.
You hesitate to change it because you’re not sure what you might break.

Importantly, foggy does not mean broken. The function may be:

Well-tested
In production
Not causing visible bugs

But like literal fog, it obscures the terrain. You can move through it, but you’re slower, more cautious, and more likely to trip.

In other words, foggy functions are early warnings. They signal design or maintainability problems long before they show up as outages.

Why Foggy Functions Matter More Than You Think

Foggy functions seem harmless at first: "I’ll just leave it; it works." But over time they carry a real cost.

1. They hide deeper design issues

Code smells rarely exist in isolation. A foggy function often points to:

Mixed responsibilities (business logic interwoven with I/O, validation, and formatting)
Leaky abstractions (low-level details bleeding into high-level operations)
Poor boundaries (function doing work that belongs to other modules)

You may see only a confusing function today, but underneath it is a design that will become harder to evolve.

2. They slow everyone down

Every time a developer visits that function, they pay a cognitive tax:

More time to read and understand
More time to reason about impact
More time to test because they’re less confident

Multiply that over months and multiple team members, and one foggy function becomes a recurring drag on velocity.

3. They silently shape the rest of the codebase

Developers copy what they see. If a confusing pattern “works” and isn’t challenged, it spreads:

Cut‑and‑paste of foggy logic into new features
New modules mirroring the same unclear structure

Before you know it, the foggy style is now the implicit standard.

The Case for a Lightweight Ritual

You don’t need a heavyweight process to address this. In fact, over‑engineering quality controls can backfire, turning review into a bureaucratic bottleneck.

What you want instead is:

Lightweight – takes minutes, not hours
Repeatable – easy to apply in every review
Consistent – everyone uses the same lens

That’s where the Foggy Function Detector comes in: a small, mental checklist you run whenever you touch or review code. Think of it as a quick “clarity scan” to identify functions that need attention.

When this becomes a team ritual, you get:

Faster, more focused code reviews
Earlier detection of maintainability problems
A shared language for discussing confusing code

The Foggy Function Detector: A Simple Checklist

Use this checklist during code review or when you’re working on adjacent code. You don’t need to run it on the entire codebase at once—just apply it incrementally whenever you’re already looking at a function.

Ask yourself these questions:

1. Can I explain this function in one short sentence?

“This function [does what] for [whom/what] under [conditions].”

If your explanation sounds like:

“Well, it kind of does A, but also B, and then sometimes C depending on…"

…you may have too many responsibilities packed into one function.

Signal: Consider splitting it up or extracting helpers.

2. Do I need to scroll or jump around to follow the logic?

If understanding the control flow requires constant scrolling, jumping to definitions, or jumping between files, the function is probably too long or too entangled.

Clarity smell indicators:

Nested if/else blocks several levels deep
Interleaving of concerns (e.g., database queries mixed with UI formatting and business logic in one place)

Signal: Extract smaller functions, flatten conditionals, or separate concerns.

3. Are the names doing enough work?

A function may be foggy simply because names are unclear:

Vague function names (processData, handleRequest)
Ambiguous variables (flag, data, list)
Unclear side effects (a function named calculate that also writes to a database)

Signal: If you need comments to explain what a better name could reveal, rename things.

4. Is there hidden state or surprising side effects?

A function reads clearly when:

Inputs and outputs are explicit
Side effects are obvious and intentional

Fog creeps in when:

The function mutates shared state you didn’t expect
It depends on global configuration or hidden singletons
It logs, sends events, or writes to disk without a clear indication

Signal: Make side effects explicit, or split pure logic from effectful behavior.

5. Does the function have more than one “reason to change”?

Ask: “What could cause us to edit this function in the future?”

If the list includes multiple unrelated reasons—business rule changes, UI tweaks, infrastructure changes—you’re mixing concerns.

Signal: Apply the Single Responsibility Principle: split the function according to different reasons to change.

6. Does this function surprise me?

Surprise is a powerful smell detector. If anything about the function’s behavior, inputs, outputs, or side effects is surprising when you read it, the code violates the principle of least astonishment.

Signal: Change names, reorganize logic, or move operations to make behavior match expectations.

Embedding the Detector into Your SDLC

Detecting foggy functions shouldn’t be an ad‑hoc effort. To really pay off, it must be part of your regular SDLC governance.

Here are some practical ways to integrate it:

1. Make it a standard part of code review

Add a short section to your review guidelines:

Foggy Function Check
For any non-trivial function you touch:

Can you summarize it in one sentence?

Are responsibilities clearly separated?

Are side effects explicit?

Encourage reviewers to flag functions as “foggy” even when they work fine, and to propose incremental refactors.

2. Use a “boy scout rule” for fog

Like the classic rule “leave the campground cleaner than you found it”, adopt a team rule:

If you touch a foggy function, leave it a bit clearer than before.

This doesn’t mean full rewrites. It might be as simple as:

Renaming variables
Extracting a small helper function
Adding a test that clarifies intent

Small, constant improvements keep the fog from thickening.

3. Track foggy areas as explicit tech debt

When a function is clearly foggy but can’t be fixed right away:

Create a small tech debt ticket
Tag it with foggy-function (or similar)
Add a one-sentence description of why it’s foggy

This turns vague discomfort into visible, actionable work you can prioritize.

How AI-Assisted Tools Can Help

AI-assisted code review is particularly well-suited to enforcing and amplifying lightweight rituals like the Foggy Function Detector.

Here’s how you can leverage AI:

1. Automated clarity scans in pull requests

Configure AI review tools to:

Flag overly long or deeply nested functions
Highlight functions whose names don’t match their behavior
Point out mixed concerns in a single function

The AI doesn’t replace human judgment, but it serves as a first pass filter that consistently surfaces likely foggy areas.

2. Generate candidate refactorings

When a function is flagged as foggy, AI tools can:

Suggest alternative names for functions and variables
Propose extracting helper functions or reorganizing logic
Provide before/after examples to help reviewers see the improvement

This reduces the friction of “knowing it’s bad” but not being sure how to improve it.

3. Institutionalize your checklist

Many AI tools allow custom rules or prompts. Encode your Foggy Function Detector into the tool itself:

Turn the checklist into a custom analysis profile
Ask the tool to evaluate new or changed functions against those criteria

Now your ritual is embedded in the pipeline, not just in people’s heads.

Conclusion: Treat Clarity as a First-Class Outcome

Confusing code rarely announces itself with errors. It shows up as that subtle sense of unease when you read a function and think, “This works, but I really don’t want to touch it.” That is your chance to act early.

A foggy function is more than a minor annoyance—it’s a leading indicator of deeper design and maintainability issues. By adopting a lightweight, repeatable Foggy Function Detector ritual, you:

Catch those early warnings before they become systemic problems
Make code reviews faster and more effective
Build a shared culture that values clarity as much as correctness

Combined with AI-assisted review tools, this ritual becomes easier to apply consistently, even as your codebase and team grow.

You don’t need to boil the ocean. Start small: on your next pull request, run the Foggy Function Detector on just one function. If it feels foggy, clear a little of that mist. Over time, your codebase—and your future self—will thank you.