Code Search Assistant

Code Search Assistant

Overview

Search codebases to find code related to a given snippet using multi-dimensional similarity analysis: call chain patterns, textual structure, and functional behavior. Results are ranked and presented with matching code snippets.

Workflow

1. Analyze Input Snippet

Extract key characteristics from the provided code snippet:

Structural elements:

• Function/method calls made
• Classes/types used
• Control flow patterns (loops, conditionals, try-catch)
• Data structures (arrays, objects, maps)

Functional elements:

• Purpose/intent of the code
• Input/output behavior
• Side effects (I/O, state changes, API calls)
• Domain concepts (authentication, validation, transformation)

Textual elements:

• Variable and function names
• String literals and constants
• Comments and documentation
• Code tokens and keywords

2. Define Search Scope

Determine where to search:

• Full repository: Search all code files
• Specific directories: Focus on relevant modules
• File type filter: Limit to specific languages

Use Glob to identify candidate files:

**/*.js, **/*.py, **/*.java, etc.

3. Search by Call Chain Similarity

Find code with similar function call patterns and dependencies.

Search strategy:

• Extract function/method calls from input snippet
• Use Grep to find files containing those function calls
• Read matching files to analyze call sequences
• Score based on:
• Number of shared function calls (weight: 40%)
• Order of function calls (weight: 30%)
• Shared imported modules/libraries (weight: 30%)

Example:

// Input snippet calls: fetch(), JSON.parse(), setState()
// High match: Code that calls fetch() → JSON.parse() → setState()
// Medium match: Code that calls fetch() and setState() in different order
// Low match: Code that only calls fetch()

4. Search by Textual Similarity

Find code with similar structure and token patterns.

Search strategy:

• Extract key identifiers from input snippet (function names, variable names)
• Use Grep to find files with similar identifiers
• Read matching files to compare code structure
• Score based on:
• Shared variable/function names (weight: 35%)
• Similar control flow structure (weight: 35%)
• Shared keywords and operators (weight: 30%)

Similarity indicators:

• Same loop patterns (for, while, forEach, map)
• Similar conditional logic (if-else chains, switch statements)
• Matching data structure operations (array methods, object access)
• Similar string/number operations

5. Search by Functional Similarity

Find code that performs similar operations or solves similar problems.

Search strategy:

• Identify the functional purpose of input snippet
• Search for code with similar purpose using semantic patterns
• Look for:
• Similar input/output transformations
• Equivalent algorithms (different implementations, same result)
• Parallel business logic
• Alternative approaches to same problem

Functional categories:

• Data transformation: Mapping, filtering, reducing, sorting
• Validation: Input checking, format validation, constraint enforcement
• I/O operations: File reading/writing, API calls, database queries
• Authentication/Authorization: Login, permission checks, token handling
• Error handling: Try-catch patterns, error recovery, logging

Search patterns:

// For validation code, search for:
- "validate", "check", "verify" in function names
- Conditional checks with error throwing
- Regular expression patterns

// For API calls, search for:
- HTTP client usage (fetch, axios, requests)
- Endpoint URLs or API patterns
- Response handling and error cases

6. Rank and Score Results

Combine similarity scores to rank results:

Scoring formula:

Total Score = (Call Chain Score × 0.35) +
              (Textual Score × 0.30) +
              (Functional Score × 0.35)

Score ranges:

• 0.8-1.0: Very high similarity (likely duplicate or variant)
• 0.6-0.8: High similarity (related implementation)
• 0.4-0.6: Medium similarity (similar patterns or purpose)
• 0.2-0.4: Low similarity (some shared elements)
• 0.0-0.2: Minimal similarity (weak connection)

Ranking adjustments:

• Boost files in same directory (+10%)
• Boost files with similar names (+5%)
• Penalize test files (-10%) unless input is a test
• Penalize generated/vendor code (-20%)

7. Format Results

Present results in ranked order with context:

Result format:

## Search Results for: [Brief snippet description]

### 1. [file_path] (Score: 0.85)

**Similarity breakdown**:
- Call chain: 0.90 (shares fetch, JSON.parse, setState calls)
- Textual: 0.75 (similar variable names and structure)
- Functional: 0.90 (performs same data fetching and state update)

**Matching code** (lines 45-62):

[relevant code snippet from the file]

**Why it matches**: [Brief explanation of similarity]

---

### 2. [file_path] (Score: 0.72)
[... repeat format ...]

Output guidelines:

• Show top 10 results by default
• Include file path with line numbers
• Show relevant code snippet (10-20 lines)
• Explain why each result matches
• Group results by score tier if many results

Search Optimization Tips

For better call chain matching:

• Include import statements in input snippet
• Provide complete function calls with arguments
• Include chained method calls

For better textual matching:

• Use descriptive variable names in input
• Include comments describing intent
• Provide complete code blocks, not fragments

For better functional matching:

• Describe what the code does in comments
• Include typical input/output examples
• Show error handling patterns

Example Usage

Input snippet:

async function fetchUserData(userId) {
  try {
    const response = await fetch(`/api/users/${userId}`);
    const data = await response.json();
    return data;
  } catch (error) {
    console.error('Failed to fetch user:', error);
    return null;
  }
}

Search process:

• Call chain: Search for fetch(), response.json(), console.error()
• Textual: Search for async functions with try-catch, similar variable names
• Functional: Search for API data fetching patterns, error handling

Expected results:

• Other API fetch functions (high similarity)
• Data retrieval functions using different libraries (medium similarity)
• Functions with similar error handling (low-medium similarity)

Tips

• Start with a complete, representative code snippet (10-30 lines)
• Include context (imports, surrounding code) for better matching
• For large codebases, narrow search scope to relevant directories
• Adjust score weights based on what matters most (calls vs. structure vs. purpose)
• Review medium-scored results (0.4-0.6) for unexpected but useful matches
• Use results to discover alternative implementations or refactoring opportunities