PyTreeSitterAttack for Code Edit Distance #106
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Summary: Add code similarity analysis infrastructure to PrivacyGuard for measuring code memorization via AST structural comparison. This diff introduces: - `PyTreeSitterAttack`: A new attack node that parses target and model-generated code into Abstract Syntax Trees (ASTs) using tree-sitter, then converts them into zss (Zhang-Shasha) Node trees for downstream tree edit distance analysis. Supports Python and C++ via a language registry with explicit imports. Detects parse failures via tree-sitter's `has_error` flag and gracefully marks them rather than crashing. - `CodeSimilarityAnalysisInput`: A new `BaseAnalysisInput` subclass that stores the generation DataFrame with AST columns (`target_ast`, `generated_ast`, `target_parse_success`, `generated_parse_success`), following the existing `TextInclusionAnalysisInput` pattern. - Pins tree-sitter to v0.25.0 in PACKAGE files for the newer Language/Parser API. Differential Revision: D93109033
Summary: Add code similarity analysis infrastructure to PrivacyGuard for measuring code memorization via AST structural comparison. See https://arxiv.org/html/2404.08817v1 This diff introduces: - `PyTreeSitterAttack`: A new attack node that parses target and model-generated code into Abstract Syntax Trees (ASTs) using tree-sitter, then converts them into zss (Zhang-Shasha) Node trees for downstream tree edit distance analysis. Supports Python and C++ via a language registry with explicit imports. - **Partial AST support**: Instead of rejecting malformed code entirely, `parse_code` now leverages tree-sitter's error recovery to produce partial ASTs by filtering out ERROR and MISSING nodes. This allows downstream similarity analysis to still detect code memorization even when model-generated code contains syntax errors. Each record is tagged with a `parse_status` of `"success"` or `"partial"` so downstream consumers can distinguish clean parses from filtered ones. - `CodeSimilarityAnalysisInput`: A new `BaseAnalysisInput` subclass that stores the generation DataFrame with AST columns (`target_ast`, `generated_ast`, `target_parse_status`, `generated_parse_status`), following the existing `TextInclusionAnalysisInput` pattern. - Pins tree-sitter to v0.25.0 in PACKAGE files for the newer Language/Parser API. Differential Revision: D93109033
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Feb 12, 2026
Summary: Add code similarity analysis infrastructure to PrivacyGuard for measuring code memorization via AST structural comparison. See https://arxiv.org/html/2404.08817v1 This diff introduces: - `PyTreeSitterAttack`: A new attack node that parses target and model-generated code into Abstract Syntax Trees (ASTs) using tree-sitter, then converts them into zss (Zhang-Shasha) Node trees for downstream tree edit distance analysis. Supports Python and C++ via a language registry with explicit imports. - **Partial AST support**: Instead of rejecting malformed code entirely, `parse_code` now leverages tree-sitter's error recovery to produce partial ASTs by filtering out ERROR and MISSING nodes. This allows downstream similarity analysis to still detect code memorization even when model-generated code contains syntax errors. Each record is tagged with a `parse_status` of `"success"` or `"partial"` so downstream consumers can distinguish clean parses from filtered ones. - `CodeSimilarityAnalysisInput`: A new `BaseAnalysisInput` subclass that stores the generation DataFrame with AST columns (`target_ast`, `generated_ast`, `target_parse_status`, `generated_parse_status`), following the existing `TextInclusionAnalysisInput` pattern. - Pins tree-sitter to v0.25.0 in PACKAGE files for the newer Language/Parser API. Differential Revision: D93109033
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Summary:
Add code similarity analysis infrastructure to PrivacyGuard for measuring code memorization via AST structural comparison.
This diff introduces:
PyTreeSitterAttack: A new attack node that parses target and model-generated code into Abstract Syntax Trees (ASTs) using tree-sitter, then converts them into zss (Zhang-Shasha) Node trees for downstream tree edit distance analysis. Supports Python and C++ via a language registry with explicit imports. Detects parse failures via tree-sitter'shas_errorflag and gracefully marks them rather than crashing.CodeSimilarityAnalysisInput: A newBaseAnalysisInputsubclass that stores the generation DataFrame with AST columns (target_ast,generated_ast,target_parse_success,generated_parse_success), following the existingTextInclusionAnalysisInputpattern.Differential Revision: D93109033