What does an AI code detector score mean?

Read the score as a review prompt

A score gives you a compact way to see whether the detector found any of its published signals in the submitted code. It can be useful for deciding where to start a review, but it cannot establish authorship or confirm that a particular AI product was used.

Code can look similar for many ordinary reasons. Developers share project templates, follow style guides, use formatters, inherit generated scaffolding, and write descriptive comments for teammates. Those patterns may overlap with a detector rule even when there is no single explanation for the result.

How Is My Code AI builds its score

Is My Code AI checks six documented code-style signals: explicit comments that name AI tools, boilerplate explanatory comments, long documentation-style comments, highly descriptive multi-word identifiers, repeated adjacent comments, and template-style defensive error text.

Each matched rule contributes its configured weight. The displayed score is the sum of those matched weights, capped at the tool's maximum display value. The score therefore describes the presence of this rule set in the code you supplied; it does not measure intent, quality, originality, or authorship.

Why a high score is not a verdict

A high score means more weighted signals matched this particular heuristic. It does not mean the code is definitely AI-generated. A readable comment, a defensive error message, or a clear identifier can be a sensible engineering choice. The relevant question is always whether the matched signal makes sense in its local context.

Use the report alongside information the heuristic cannot see: commit history, ticket context, code review discussion, project conventions, and the surrounding implementation. Those sources are often more useful for understanding how a change came to exist.

Why a low score is not clearance

A low score only means that few or none of the tool's configured signals matched. It does not prove that the code was written by a person, that it is safe, or that it has no relationship to an AI-assisted workflow. A transparent heuristic should be interpreted within the boundaries of the rules it actually runs.

Use the matched rules and flagged lines

The score is only a summary. The more useful part of a transparent report is the evidence behind it: the matched rule names and the flagged lines. Read the specific line, then read the code around it. Ask whether the pattern is intentional, conventional, or copied from a known template before drawing any conclusion.

If a signal is not meaningful in context, treat it as a limitation of the heuristic rather than evidence against the developer. If it is meaningful, it can guide a closer technical review without replacing human judgment.

Keep source code local while you review

Is My Code AI runs the analysis in the current browser. You can paste code, upload supported source files, or choose a local folder; submitted code is not uploaded by this static site. The language picker supports Python, JavaScript, Java, C++, PHP, and C#.

Try the browser-based AI code detector to see the score, matched rules, and flagged lines for a local review.