Case Study

CodeLens AI

An AI-powered repository intelligence tool for understanding unfamiliar codebases.

CodeLens AI helps developers understand large or unfamiliar repositories before making changes. Instead of forcing developers to manually inspect every folder and file, the tool analyzes structure, modules, functions, dependencies, and risk areas to present the codebase as an interactive system map.

01 THE PROBLEM

Developers often lose time when entering a new codebase. Folder structures show where files are located, but they do not explain architecture, business logic, dependencies, or the impact of changing a function. This makes onboarding slow and increases the risk of breaking existing behavior.

02 THE SOLUTION

CodeLens scans a repository, parses source files, extracts structural information using AST-based analysis, and organizes the codebase into layered views such as architecture, domains, files, functions, and dependency paths. AI-generated summaries help developers understand what each module does and where to begin.

03 KEY FEATURES
  • Repository scanning and indexing
  • Architecture, module, and domain mapping
  • File, function, and dependency extraction
  • Graph-based visual exploration of code relationships
  • AI-generated summaries for modules and functions
  • Risk-node and duplicate-logic detection concept
04 IMPACT
  • Reduces onboarding time for unfamiliar repositories
  • Helps developers understand structure before editing code
  • Turns static source files into an explorable system map
  • Improves confidence when navigating complex codebases
05 FUTURE SCOPE

Future improvements include GitHub import, multi-language parsing, pull-request impact analysis, dependency risk scoring, duplicate logic detection, and AI-generated onboarding reports for new developers.

Why reading code top-to-bottom does not scale

Large codebases are not meant to be understood linearly. Developers need entry points, relationships, and context. A folder tree shows structure, but it does not explain how the system behaves.

The core idea behind CodeLens is to convert source code into a knowledge graph. Files, functions, modules, and dependencies become connected nodes, making the codebase easier to inspect and reason about.

The project combines static analysis with AI summarization. Static analysis provides structure and relationships, while AI helps explain intent, responsibilities, and possible risk areas in natural language.

Design Note

The UI should feel like a technical analysis console. It should make complex code relationships inspectable without overwhelming the developer.

What I would improve next

Add direct GitHub repository import.

Support Java, Python, TypeScript, and C++ parsing.

Add pull-request impact analysis before code changes are merged.

Generate onboarding reports for new developers joining a project.

TECH STACK
ReactTypeScriptAST ParsingGraph ViewAI Analysis
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