Understanding AI Contribution

## Understanding AI Contribution

Modern codebases increasingly include code written or assisted by AI.

SyntaxValid analyzes AI contribution to help teams understand where AI-generated code exists and whether it introduces additional risk.

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## What AI contribution means

AI contribution represents the likelihood that parts of the codebase were generated or heavily assisted by AI tools.

This includes:

- Fully AI-generated code

- AI-assisted refactors

- Pattern-complete code produced by large language models

AI contribution is treated as a risk signal, not a judgment.

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## Why AI-generated code needs special analysis

AI-generated code can be:

- Correct but fragile

- Syntactically valid but semantically unsafe

- Overconfident in edge cases

- Inconsistent with project architecture or policies

Traditional static analysis tools do not distinguish AI-written code from human-written code.

SyntaxValid does.

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## How SyntaxValid detects AI contribution

SyntaxValid uses multiple signals to estimate AI contribution, including:

- Structural and stylistic patterns

- Predictability and entropy characteristics

- Repetitive or overly generic constructs

- Mismatch between code complexity and domain context

- Known AI-generation fingerprints

Detection is probabilistic and explainable.

Results are never binary or absolute.

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## How AI contribution affects analysis

AI contribution alone does not reduce TrustScore.

It becomes relevant when combined with:

- Security risks

- High-severity issues

- Architectural violations

- Unsafe patterns in critical paths

This prevents penalizing safe, well-reviewed AI-assisted code.

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## AI contribution and TrustScore

AI contribution influences TrustScore when it increases overall risk.

Examples:

- High AI contribution + blocking security issue → strong negative impact

- High AI contribution + clean analysis → minimal or no impact

- AI-generated code fixed and reviewed → risk reduced

TrustScore reflects risk, not authorship.

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## How teams should use AI contribution

### For developers

- Review AI-generated changes more carefully

- Validate assumptions and edge cases

- Use Fix with AI to generate safer alternatives

### For tech leads and CTOs

- Monitor AI usage trends across projects

- Identify areas where AI-assisted code needs stronger review

- Balance speed with long-term reliability

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## What AI contribution is not

AI contribution is not:

- A license compliance check

- A usage ban or enforcement mechanism

- A developer performance metric

It is a visibility and risk-awareness tool.

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## Why this matters

AI tools accelerate development.

They also change the risk profile of software.

SyntaxValid helps teams adopt AI safely and responsibly.

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## Next steps

- Issues and severities

- Fix with AI workflow

- Using SyntaxValid in daily development