From Idea to MVP: Developing the Codeflow Navigator

Introduction

Every engineer’s had that moment — they open an unfamiliar codebase and spend hours piecing together how everything fits. This idea sparked the Codeflow Navigator: an AI-assisted code-tracing tool that helps teams and new hires quickly understand where errors happen, how functions interact, and what matters most in a large project. Below is a look at how we validated this idea, defined an MVP, and planned to build + test a meaningful prototype.

1. Idea Validation

We started by asking: “What’s the real pain point?”

  • Target Users: We identified five personas (Onboarding Engineer, Debugging Pro, Tech Lead, Polyglot Dev, Knowledge Preserver).
  • Core Problem: People needed a faster, more intuitive way to trace errors and learn how code components interact, especially in large, multi-language projects.

Validation Approach

  1. User Interviews: We spoke with junior devs (struggling to onboard), senior devs (stuck on debugging logs), and architects (needing real-time code insights).
  2. Pain Points repeatedly surfaced:
    • Lack of onboarding workflows.
    • Time-consuming error-tracing across files or microservices.
  3. Key Insight: A tool that automatically visualizes dependencies and error chains would reduce confusion for everyone.

2. MVP Definition

Although we had grand visions of full AI-driven code navigation, we narrowed the scope to one primary persona: The Onboarding Engineer. Our MVP would let a new dev quickly grasp the codebase structure, highlight critical workflows, and provide short, dynamic guides.

MVP Features

  1. Codebase Overview

    • Generate a high-level summary of the project (key modules, workflows).

  2. File-Level Summaries

    • Provide short explanations of each file’s role and main functions.

  3. Dynamic Onboarding Guide

    • Offer a step-by-step intro to important files and workflows.
    • E.g., “Start with App.js, then check routes/api.js.”

  4. Dependency Visualization

    • An interactive or generated map showing how modules connect.

  5. Quick Search

    • Search for a function name/variable, see where it’s defined and used.

3. Development & Testing Plan

Phase 1: Core Functionality

Static Analysis Engine

  • Build a parser (e.g., AST-based) for a single language (like JavaScript or Python).
  • Extract functions, imports, docstrings, etc.

Data Aggregation & Summaries

  • Summarize each file:
    • “Defines validateUser() and loginHandler().”
    • “Imports db.js and logger.js.”

Technical Approach

  • Recursively scan the project to discover files.
  • Parse each file with Babel/Esprima (JS) or Python AST (Python).
  • Store results in a structured JSON object.

Phase 2: MVP Output

Dynamic Markdown File

  • Convert the JSON into a .md file (e.g., onboarding-guide.md) that includes:
    • Project overview
    • Key files
    • File-by-file summaries

Why Markdown?

  • Lightweight, easy to share, and IDE-agnostic.
  • Acts as a single onboarding doc generated whenever a new dev joins.

Phase 3: IDE Integration (Optional Future)

VS Code Plugin

  • A sidebar panel showing each file’s summary, dependencies, and “onboarding steps.”
  • Tooltips that reveal function insights on hover.

Phase 4: Advanced Features

  • Error Workflow: Paste an error log to see which file(s)/function(s) likely caused it.
  • AI Summaries: Use GPT or local models to refine “purpose” statements in the doc.
  • Workflow Overlays: Show how user actions (like logging in) travel through multiple files.

4. Testing Strategy

We adopted Test-Driven Development for the core analysis engine:

  1. Unit Tests

    • File Discovery: Ensures we only parse relevant files (no node_modules, etc.).
    • Parsing: Confirms function, import extraction is correct (no false positives).

  2. Integration Tests

    • End-to-end flow: run the tool on a mock project, confirm the generated README.generated.md matches expectations.

  3. Mock Projects

    • A small codebase with multiple interdependent files.
    • A more complex codebase with many functions to test the summarizer’s performance.

Example Test Cases

Workflow:

  • Input: python code-navigator /path/to/new_mock_project
  • Expected Output: A markdown file describing each file’s purpose, list of functions, imports, and a short recommended learning path.

5. Example MVP Workflows

Onboarding Use Case

  • Open the auto-generated README.generated.md.
  • See a big-picture “project overview”: key tech used, main directories.
  • Follow the onboarding guide: “1) Read App.js; 2) See routes/api.js.”
  • Understand the role of each file and how it’s connected.

Debug Use Case (Future)

  • Paste an error log.
  • Navigator shows which file and function might be the source.
  • Dev jumps directly to the relevant snippet, seeing how the error might propagate.

6. Conclusion & Next Steps

  1. Idea Validation: Users want a single place for codebase onboarding and error tracing.
  2. MVP: Focus on Onboarding Engineers with file summaries, a project overview, and dynamic guides.
  3. Development: Start with a static analysis engine to parse code and produce Markdown docs.
  4. Testing: Mock projects, TDD for parsing, and checks for meaningful summaries.
  5. Future: Expand to real-time error tracing, deeper AI summaries, and full IDE integration.

Final Thought: By anchoring our product design in real user needs (especially the Onboarding Engineer), we ensure the Codeflow Navigator tackles the biggest obstacles first. Over time, advanced features like error-driven insights and dependency mapping for distributed systems can evolve from the same core engine.

Want more? In the next article I'll break down the first few days of development where I finalized a very simple prototype based on this articles MVP definition. There’s a long roadmap ahead, but a solid MVP ensures we’re on the right track from day one.