Feature/new danger code #40
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Implements advanced semantic detection features to improve accuracy and reduce false positives in complex Python codebases. Core Analysis Improvements: - Added ABC class and abstract method tracking to prevent flagging them as dead. - Implemented a Protocol duck-typing engine to identify classes structurally implementing protocols. - Refined Enum member matching to prioritize qualified attribute references (Enum.Member) and removed bare-name fallback to prevent false matches. - Added detection for optional dependency flags (HAS_*/HAVE_*) within try...except ImportError blocks. - Implemented confidence penalties for lifecycle methods (on_*, watch_*, compose) and Adapter classes. - Set confidence to 0 for methods that only raise NotImplementedError. API & Integration: - Achieved parity between [process_single_file](cci:1://file:///e:/Github/CytoScnPy/cytoscnpy/src/analyzer/single_file.rs:26:4-363:5) and the [analyze_code](cci:1://file:///e:/Github/CytoScnPy/cytoscnpy/src/analyzer/single_file.rs:365:4-614:5) API by porting enum and duck-typing logic. - Fixed optional-dependency import aliasing to correctly record references for simple aliases (e.g., 'pd' for pandas). - Improved CFG data flow precision by using accurate line numbers for elif tests. Benchmark & Verification: - Expanded benchmark suite with modern Python examples (ABC, Protocols, TypeAliases, Enums). - Updated ground truth to reflect new features and maintain a rigorous evaluation baseline. - Verified F1 score improvement to ~0.75 on the expanded test corpus. Maintenance: - Updated documentation navigation and dependencies. - Refactored visitor logic to use more efficient string operations.
Implements advanced semantic detection features to improve accuracy and reduce false positives in complex Python codebases. Core Analysis Improvements: - Added ABC class and abstract method tracking to prevent flagging them as dead. - Implemented a Protocol duck-typing engine to identify classes structurally implementing protocols. - Refined Enum member matching to prioritize qualified attribute references (Enum.Member) and removed bare-name fallback to prevent false matches. - Added detection for optional dependency flags (HAS_*/HAVE_*) within try...except ImportError blocks. - Implemented confidence penalties for lifecycle methods (on_*, watch_*, compose) and Adapter classes. - Set confidence to 0 for methods that only raise NotImplementedError. API & Integration: - Achieved parity between [process_single_file](cci:1://file:///e:/Github/CytoScnPy/cytoscnpy/src/analyzer/single_file.rs:26:4-363:5) and the [analyze_code](cci:1://file:///e:/Github/CytoScnPy/cytoscnpy/src/analyzer/single_file.rs:365:4-614:5) API by porting enum and duck-typing logic. - Fixed optional-dependency import aliasing to correctly record references for simple aliases (e.g., 'pd' for pandas). - Improved CFG data flow precision by using accurate line numbers for elif tests. Benchmark & Verification: - Expanded benchmark suite with modern Python examples (ABC, Protocols, TypeAliases, Enums). - Updated ground truth to reflect new features and maintain a rigorous evaluation baseline. - Verified F1 score improvement to ~0.75 on the expanded test corpus. Maintenance: - Updated documentation navigation and dependencies. - Refactored visitor logic to use more efficient string operations.
…, taint analysis, and extensive testing infrastructure.
…alysis, type inference, and security rules, alongside new benchmarks and documentation.
… covering various vulnerabilities.
… covering various vulnerabilities.
…ation, and initial Rust analyzer components.
…tics for improved static analysis.
…is, including basic blocks, fingerprinting, and data flow, alongside new analysis components and tests.
…d initial dangerous code detection rules for injection, network, and filesystem vulnerabilities.
…d initial dangerous code detection rules for injection, network, and filesystem vulnerabilities.
…nd taint analysis rules, comprehensive testing, reporting, and VS Code integration.
Dependency Review✅ No vulnerabilities or license issues or OpenSSF Scorecard issues found.Scanned FilesNone |
Benchmark ResultsFailed to read benchmark results. |
Contributor
Summary of ChangesHello @djinn09, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request delivers a substantial upgrade to the project's security analysis capabilities, specifically by overhauling the 'danger' rule set and its integration with taint analysis. The changes introduce a more robust and maintainable rule system, refine the accuracy of unused code detection, and provide comprehensive documentation for all security and quality checks. Additionally, it includes updates to development tooling and editor integration for a smoother developer experience. Highlights
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Code Review
This pull request introduces several enhancements and bug fixes to the CytoScnPy project, focusing on security, code quality, and performance. Key changes include taint analysis improvements, new security rules, updates to the VS Code extension, and various code quality enhancements. The review focuses on identifying potential issues related to security vulnerabilities, code maintainability, and adherence to coding best practices, with a particular emphasis on areas where improvements can be made.
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