To check if code is copied, compare it with likely source material using code-aware search or similarity tools, then review the matching sections in context.
A similarity score can point you toward evidence, but it cannot establish who wrote the code or whether a rule was broken.
What does a code similarity check show?
A code similarity check identifies overlapping structure or text between files, repositories, or a submitted program and a reference collection.
Depending on the method, it may flag identical lines, renamed variables, reordered statements, or similar program structure.
The result is a lead for investigation, not a verdict.
Some overlap is expected.
Many programs use standard library calls, common algorithms, framework conventions, starter code, or examples from documentation.
Two developers solving the same small problem may also produce similar code independently.
Interpret every match against the assignment, project history, and applicable rules.
How to check if code is copied
- Identify the code you are allowed to compare. Keep the submitted files intact and note their source, date, and relevant project or assignment.
If you have permission, compare them with the suspected original, earlier versions, or an approved repository collection. - Search for distinctive snippets. Select a few nontrivial lines, such as an unusual condition, error message, or sequence of operations, and search them in quotation marks with a general web search.
Remove names and formatting that may vary.
Common expressions and short fragments are weak evidence. - Compare files with a diff tool. A text diff can reveal identical lines and changes between two known files.
For code, ignore whitespace and formatting where appropriate, but remember that a simple diff may miss renamed variables or rearranged logic. - Use a code similarity tool for larger comparisons. Code-aware systems can compare multiple files and highlight regions that resemble one another despite some edits.
Provide only material you are authorized to submit, and check how the service handles uploaded code before sharing private or sensitive work. - Review each flagged match manually. Open both versions and check whether the overlap is a common idiom, required template, generated file, or distinctive implementation.
Look at the surrounding logic, comments, variable choices, and the size and distribution of matching sections. - Preserve the evidence and ask for context. Record the compared files, tool output, and relevant dates.
If the question concerns a class or workplace, follow the applicable review process and give the author a chance to explain shared templates, collaboration, or other legitimate sources.
Which methods are useful for different cases?
For a small public snippet, exact-text search may quickly locate a likely source.
For two known versions, a local diff is simple and transparent.
For many submissions or large repositories, a code similarity detector can help prioritize which pairs deserve review.
Each method has limits: search depends on material being indexed, diffs focus on textual changes, and detectors may flag routine patterns or miss heavily transformed code.
Codespy is an external source-code detector that may be relevant when researching AI-authorship classification.
Do not assume it performs source-code similarity comparisons. This article does not report a hands-on evaluation or verify current product capabilities.
Check the provider’s current documentation and data-handling terms before using it.
How should you interpret a similarity report?
Look beyond any headline score.
Check which passages match, whether the matches are independent or concentrated in one required section, and whether the code’s structure is distinctive.
A report may count common boilerplate or standard methods, while a low overall score can still contain one substantial, unusual match worth examining.
Consider alternative explanations.
Did the project provide starter files?
Were developers allowed to collaborate?
Could both versions come from a public tutorial or shared library?
Do comments, commit history, or earlier drafts help establish how the work developed?
These details can change what a match means.
Do not treat a similarity percentage as a probability of copying, an authorship label, or proof of intent.
Tools measure resemblance according to their own methods and reference data.
Human review is necessary, and decisions about academic or workplace misconduct belong to the relevant institution or organization under its policies.
Is AI code detection the same as plagiarism checking?
No.
AI authorship classifiers attempt to estimate whether text or code may have been generated by an AI system.
Similarity tools look for overlap with other material.
A program can be AI-generated without matching a known source, or copied from a human-written source without being AI-generated.
Turnitin has stated that its AI writing model does not reliably detect code.
Its AI writing report should not be confused with a source-code similarity check.
If you need to investigate overlap in a program, use an approach designed to compare code and review the underlying matches rather than relying on a writing classifier.
What should you avoid when checking code?
- Do not accuse someone based on a score alone. Confirm the matched passages and consider legitimate sources of overlap.
- Do not upload confidential code casually. Review the tool’s privacy, retention, and access terms first, especially for proprietary, personal, or unpublished work.
- Do not assume policies are universal. Schools, employers, and open-source projects set different rules for assistance, attribution, reuse, and review.
- Do not overlook licenses and attribution. A source match may raise a licensing or citation question even when the code was reused openly; check the relevant license and project rules.
A careful check combines targeted searches, direct comparison, and contextual review.
The strongest next step is to document exactly what matched and verify its source before drawing conclusions.