Code Analysis Tool
Relevant source files
The following files were used as context for generating this wiki page:
The Code Analysis Tool is a core module within the CodeScholar project designed to evaluate source code quality, extract technical concepts, and link codebases to relevant academic research. It provides a multi-layered approach to code evaluation, ranging from basic pattern matching to advanced semantic linking via a Model Context Protocol (MCP) server.
The tool serves two primary functions: first, it identifies algorithms, data structures, and research concepts within a user's code; second, it utilizes static analysis metrics to provide quantitative assessments of code maintainability, readability, and complexity. This enables developers to understand the theoretical foundations of their implementation while maintaining high software engineering standards.
Sources: server.js:433-441, knowledge/techniques/static-code-analysis.json:5-10
Architecture and System Flow
The system architecture follows a client-server model where an MCP server handles requests to analyze local files or directories. The logic is split between a Node.js server that manages the tool lifecycle and external Python scripts that perform the deep AST (Abstract Syntax Tree) parsing and concept extraction.
Analysis Pipeline Flow
The following diagram illustrates the flow from a tool call to the final generation of linked paper results.
The system leverages a hybrid search mechanism that combines keyword-based text matching with vector-based semantic search to ensure high relevance in results.
Sources: server.js:107-115, server.js:527-540, scripts/generate_knowledge_base.py:843-855
Core Analysis Components
1. Static Code Analysis (SCA)
Static Code Analysis evaluates code quality without program execution. It scans source code to identify "code smells," potential errors, and violations of coding standards. The tool specifically focuses on three key metrics: readability, maintainability, and complexity.
| Metric | Target | Description |
|---|---|---|
| Readability | Syntax/Style | Evaluates how easily a human can understand the code structure. |
| Maintainability | Architecture | Measures the ease with which code can be modified or extended. |
| Complexity | Logic | Often measured via Cyclomatic Complexity to track logical branching. |
Sources: knowledge/techniques/static-code-analysis.json:5-15, knowledge/papers/does-chatgpt-help-novice-programmers-write-better-code-results-from-static-code-.json:27-33
2. Concept Extraction and Linking
The tool analyzes source code to extract higher-level research concepts. This is achieved through the analyze_code and link_papers_to_code tools.
- Extraction: The system uses Python-based analyzers (e.g.,
concept_extractor.py) to parse files. It supports Python (via AST parsing), JavaScript, and TypeScript. - Linking: Once concepts (like "gradient descent" or "microservices") are identified, the
QueryGeneratorcreates optimized search queries to probe the local paper library. - Scoring: The
ApplicabilityScorerranks papers based on how well the identified research matches the specific implementation in the code.
Sources: server.js:433-460, server.js:527-560
Tool API and Configuration
The MCP server exposes several tools for programmatic code analysis.
Tool Definitions
| Tool Name | Parameters | Description |
|---|---|---|
analyze_code | file_path, directory, search_library | Extracts algorithms/concepts and generates search queries. |
link_papers_to_code | file_path, max_papers, min_score | High-level tool that links code to ranked academic papers. |
search_papers | query, limit, year, min_citations | Performs keyword or hybrid search across the library. |
Sources: server.js:337-375, server.js:433-455, server.js:500-520
Interaction Sequence
The following sequence diagram shows the interaction between the MCP server and the internal scoring engine during a linking request.
Sources: server.js:527-610
Implementation Details
The server utilizes a hybrid search mode. If an OpenAI API key is present, it attempts to generate embeddings for semantic search; otherwise, it falls back to a weighted text search.
// Example of the scoring logic used in textSearch
for (const term of queryTerms) {
if (entry.title_lower.includes(term)) score += 3;
if (entry.abstract_lower.includes(term)) score += 2;
if (entry.authors_lower.includes(term)) score += 1;
}
if (entry.title_lower.includes(query.toLowerCase())) score += 5;
Sources: server.js:192-205
The Code Analysis Tool serves as the bridge between raw implementation and academic theory, providing both quantitative quality metrics and qualitative research context to the developer. It is particularly effective for evaluating legacy systems or code generated by AI tools.
Sources: knowledge/problems/code-quality-assessment.json:15-25, server.js:630-650