Git commits can help document how a codebase changed over time, but they do not independently prove who wrote the code.
Understanding what commit metadata, review records, and code analysis can show helps separate useful evidence from claims of certainty.
What a Git commit records
Git is a distributed version control system.
A commit stores a snapshot of tracked files, a reference to its parent commit or commits, and metadata such as an author name, email address, timestamp, and commit message.
A separate committer field can identify the person or process that created or applied that commit.
In routine work, author and committer may be the same person; in a patch workflow, they can differ.
This information describes what Git was told when the commit was created.
Names and email addresses are configurable text, timestamps come from the local environment, and commit messages are written by the person or process making the change.
They can make a history informative, but they are not automatic identity checks.
What commit history can support
A coherent repository history may support a timeline of development.
For example, related commits can show when a feature appeared, how it was revised, and which changes were included in a branch or release.
When preserved in a hosted platform or organizational system, associated pull requests, code reviews, issue discussions, and account records can add context about participation.
That context can be useful in a project handoff, an internal review, or an investigation of how a particular change entered a codebase.
Evidence is stronger when independent records agree: repository history, review comments, signed releases, build logs, and documented contributor accounts may jointly support a narrative.
Each record still has limits, and its reliability depends on how it was created and preserved.
Why commits do not prove authorship
A Git author field is not the same as a verified human identity.
A user can set it to another name, reuse someone else's email address, or make commits through automation.
A commit can also be copied, rebased, cherry-picked, squashed, or amended, changing its context or metadata along the way.
A commit may represent code written by one person, edited by another, generated by a tool, or assembled from several sources.
Cryptographic commit signing can help establish that a commit was signed with a particular key, assuming the key and its ownership are verified and remained secure.
It does not, by itself, establish that the signer personally authored every line.
Signing authenticates an assertion about the commit; it does not certify the origin of the code or reveal whether an AI assistant contributed.
Can Git show whether code was written by AI?
Git history can sometimes provide clues about a development process, such as a large generated change followed by human edits, but those patterns are not conclusive.
AI-assisted code can be added incrementally, and human-written code can arrive in a single large commit.
Commit messages, timing, and coding style are not dependable standalone tests of AI involvement.
AI authorship classification and code similarity analysis answer different questions.
An AI classifier estimates whether text or code may have been produced with AI.
A similarity or plagiarism check looks for overlap with other material, such as published code or a submitted source.
A match may warrant checking attribution and licensing, but it does not establish that AI generated the code.
Likewise, an AI-likelihood score is not proof of authorship or misconduct.
What other evidence can help establish provenance?
For a careful review, preserve the repository and examine its history alongside records that provide independent context.
Relevant material may include:
- Pull requests and code reviews: They may document who proposed, discussed, and approved a change.
- Issue trackers and design notes: They can connect implementation decisions to earlier planning and requirements.
- Build and release records: They can help identify which source revision produced a particular artifact.
- Contributor statements and project records: These may clarify collaboration, tool use, and attribution, subject to the applicable process.
- Source comparison: Similarity checks can identify overlapping code for further investigation, while leaving questions of permission, authorship, and intent to be assessed separately.
Maintain original evidence where possible, record how and when it was collected, and avoid relying on screenshots or exported snippets alone.
If a decision has legal, employment, or academic consequences, use the relevant review process and evaluate the full record.
Policies vary by organization and institution.
How code detectors fit into an authorship review
Code detection products should be treated as one possible source of leads, not as arbiters of authorship.
Codespy currently lists JavaScript, Python, Java, PHP, C/C++, and C# among its supported languages.
If considering it, review its current documentation and terms to understand what it analyzes, what its results mean, and what data handling information it provides.
No detector score should be presented as proof that a named person wrote code, used AI, or violated a policy without supporting evidence.
Turnitin has stated that its AI writing model does not reliably detect code.
Its AI writing report should not be confused with source-code similarity tools, which compare code for overlap rather than determine who created it.
Before using any tool, verify that it is designed for the material and question at hand, and follow applicable privacy and institutional requirements.
How to describe Git evidence accurately
Precise wording keeps evidence in proportion to what it shows.
Rather than saying a commit “proves” someone wrote a feature, describe the observable facts: the repository lists a particular author, the commit changes specific files, and a linked review records discussion by named accounts.
Then explain what remains unverified, including the identity behind an account and the origin of individual code sections.
Git commits can establish a useful, inspectable record of change when preserved with trustworthy supporting context.
On their own, they establish what a repository records—not definitive human authorship or whether AI played a role.