Client spotlight
Deep Codebase Review: Sample Diagnostic Report
TechCorp Industries · Enterprise SaaS
TechCorp Industries engaged GitPlumbers for a comprehensive diagnostic review of their AI-scaffolded platform. Our deep codebase analysis uncovered critical technical debt patterns, security vulnerabilities, and architectural risks—providing a clear roadmap to transform their fragile MVP into a production-hardened application ready for enterprise scale.
- TypeScript
- React
- Node.js
- MongoDB
- AWS
Challenge
A rapidly growing startup's AI-generated MVP had evolved into a complex codebase with mounting technical debt, architectural inconsistencies, and concerning dependency patterns that threatened scalability and maintainability.
Approach
- Conducted comprehensive static analysis across 450K lines of code to identify technical debt hotspots and code smells.
- Generated dependency heatmaps revealing critical coupling issues and over-reliance on deprecated packages.
- Applied AI-artifact detection algorithms to surface untested, fragile code patterns likely generated by AI assistants.
- Performed security audit on 187 dependencies, flagging 23 critical vulnerabilities and 15 deprecated packages.
- Analyzed code complexity metrics, identifying 34 modules with cyclomatic complexity above safe thresholds.
- Created risk scoring matrix combining test coverage, dependency age, complexity, and change frequency.
Outcomes
- Identified 156 instances of duplicate logic across components, reducing bundle size opportunity by 18%.
- Surfaced 23 security vulnerabilities requiring immediate attention, with clear remediation paths.
- Discovered 12 critical paths where changes could cascade failures across 40+ modules.
- Found 67 AI-generated functions lacking error handling, input validation, or edge case coverage.
- Mapped 8 architectural bottlenecks preventing horizontal scaling under load.
- Delivered prioritized remediation roadmap with effort estimates and business impact analysis.
23
critical security vulnerabilities identified in dependency chain.
67
AI-generated code artifacts flagged for review and hardening.
156
code duplication instances mapped for consolidation.
18%
potential bundle size reduction through deduplication.
Comprehensive Analysis Results
Codebase Overview
Language Distribution
Complexity Heatmap
Modules sorted by cyclomatic complexity - higher scores indicate higher maintenance risk
Dependency Health Analysis
Critical vulnerabilities and deprecated packages requiring immediate attention
AI-Generated Code Artifacts
Files with high confidence of AI generation requiring manual review and hardening
- No error handling
- Missing input validation
- Untested edge cases
- Inconsistent error responses
- No rate limiting
- SQL injection risk
- Hardcoded values
- No retry logic
- Missing logging
- Race conditions
- Memory leaks possible
- No cleanup
Test Coverage Analysis
Coverage by Module
Technical Debt Score
Moderate technical debt - prioritize key areas