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c-code-score gives each C function a single structural score to help rank what a person should inspect or consider rewriting. Its author, Jens Harms, proposes using the same score to guide a quick LLM rewrite loop. It is a triage aid—not a bug detector, proof of safety, or measure of correctness.
What c-code-score measures
The score combines three structural signals:
score(f) = nesting × pointer depth × deref chain
- Nesting: the depth of nested
if,for, andwhilelevels. - Pointer depth: pointer indirection in parameters and local variables, such as
int *,int **, andint ***. - Dereference chain: runs of member dereferences such as
a->b->c.
These are proxies for structural complexity. A high score is a reason to look more closely, not a probability that the function contains a defect. The tool is intentionally small and does not use a substantial parser; Harms describes its parsing as imperfect.
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How to use the score for review
The practical use is to rank functions, then inspect the highest-scoring ones. A ranking can help focus a review or identify candidates for refactoring when a codebase is too large to examine uniformly.
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- Read the flagged function in context, including its callers and the data it manipulates.
- Check whether its branching, indirection, or member access can be made clearer without changing behavior.
- Use tests and code review to validate any change; a lower score alone does not establish that the rewrite is correct.
Harms explicitly cautions that the score cannot find semantic bugs and does not account for deep call stacks full of side effects. Those omissions matter: a short, low-scoring function can still behave incorrectly, while a complex function may be necessary and well-tested.
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Trying the Python package
The package is published on PyPI as c-code-score. The retrieved package listing showed version 0.1.2, Python 3.8 or newer, and an MIT license; package metadata can change. The listing describes it as a dependency-free, single-file Python script.
pip install c-code-score
c-score path/to/file.cUse the command-line scorer on the C files you want to rank, then review the output rather than treating the ranking as an automated finding. Exact command options and parser coverage are not established here, so check the package’s current documentation for details beyond this basic example.
Using it as an LLM feedback loop
Harms suggests a bounded cycle for LLM-generated C:
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- Run
c-scoreon it. - Ask the model to rewrite the three highest-scoring functions more simply.
- Score the result again and inspect what changed.
This turns a vague request such as “make this less complex” into a concrete task: target a small set of functions and check whether their structural scores moved. The author reports that one round visibly flattened the output, but that is his observation, not an independently reproduced before-and-after result. A reduced score is useful feedback only if tests, review, and behavior checks show the rewrite preserved the program’s intent.
What the reported churn figures show—and do not show
Harms reports comparing function scores with maintenance churn—how often a function was touched—in libXt and libtiff. His 2026 figures are:
| Measure compared with churn | libXt | libtiff |
|---|---|---|
| c-code-score | Spearman correlation 0.52 | Spearman correlation 0.38 |
| Line count | Spearman correlation 0.50 | Spearman correlation 0.33 |
| Cyclomatic complexity | Spearman correlation 0.41 | Spearman correlation 0.32 |
He also reports that the 15 highest-scoring functions had roughly three to five times the churn of the 15 lowest-scoring functions. These are the author’s measurements, not independently verified results. They indicate an association with past edits in those projects; they do not show that the score predicts bugs, that complexity causes churn, or that refactoring a highly ranked function will improve maintainability.
In a separate observation, Harms says he examined more than 20 years of Git history in libtiff, curl, Redis, and OpenMotif and found median function size stayed flat while the largest function grew. That is also his reported analysis, not a result independently reproduced here.
When this tool is—and is not—the right choice
c-code-score is most useful when low setup friction and an explainable ranking are more valuable than deep program analysis. Its formula makes it easy to understand why a function might rise in the list, but that simplicity limits what it can see.
Quick Recap
Best Value
- Use it for: prioritizing human inspection, finding structurally tangled functions, or giving an LLM a finite rewrite target.
- Do not use it as: evidence that code is safe, a substitute for tests or review, or a replacement for static analysis when semantic or interprocedural issues matter.
- Interpret it carefully: parsing is imperfect, and the score ignores important behavior that cannot be inferred from nesting, pointer indirection, and member-access chains alone.
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