Understanding Analysis Results

When BambooSnow analyzes your repository, it generates detailed insights about your codebase. This guide explains what each metric means and how to use these insights.

Analysis Overview

The analysis dashboard shows a high-level summary:

• Health Score: Overall code health from 0-100
• Languages: Detected programming languages and their percentages
• Framework Detection: Identified frameworks and libraries
• Agent Recommendations: Suggested agents based on your codebase

Code Structure Analysis

Repository Metrics

| Metric | Description | |--------|-------------| | Total Files | Number of source code files | | Lines of Code | Total LOC excluding comments and blanks | | Complexity Score | Cyclomatic complexity average | | Test Coverage | Estimated test file coverage |

Language Breakdown

BambooSnow detects languages by file extension and content analysis:

TypeScript   45.2%  ████████████████░░░░
Python       32.1%  ███████████░░░░░░░░░
JavaScript   15.4%  █████░░░░░░░░░░░░░░░
CSS           7.3%  ██░░░░░░░░░░░░░░░░░░

Bottleneck Detection

BambooSnow identifies areas that could benefit from automation:

Common Bottlenecks

1. Missing Tests

   • Files with no corresponding test files
   • Low test-to-code ratio
   • Recommended: Test Generator Agent

2. Security Vulnerabilities

   • Outdated dependencies
   • Known CVEs in packages
   • Recommended: Security Scanner Agent

3. Code Style Inconsistencies

   • Mixed formatting patterns
   • Naming convention violations
   • Recommended: Code Reviewer Agent

4. Dependency Issues

   • Outdated packages
   • Unused dependencies
   • Recommended: Dependency Updater Agent

Recommendations

Based on the analysis, BambooSnow suggests specific actions:

High Priority

Issues that should be addressed soon, such as security vulnerabilities or critical bugs.

Medium Priority

Improvements that would enhance code quality, like adding tests or updating dependencies.

Low Priority

Nice-to-have improvements like code style standardization.

Re-running Analysis

Analysis runs automatically when:

• A repository is first connected
• Significant changes are pushed to the main branch
• You manually trigger a re-analysis

To manually re-run:

1. Go to your repository dashboard
2. Click Analyze in the top right
3. Wait for the analysis to complete (1-3 minutes)

Exporting Results

Export your analysis results for team review:

• PDF Report: Comprehensive summary with charts
• JSON Export: Raw data for custom integrations
• CSV Export: Spreadsheet-friendly format