InterviewOS

DSA-Driven Coding Interview Preparation Engine

InterviewOS is an intelligent interview preparation system that recommends coding problems based on a user’s performance using core Data Structures and Algorithms (DSA).
The project emphasizes algorithmic reasoning, adaptive learning paths, and system design, not UI complexity.

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Project Overview

Unlike linear problem lists, InterviewOS models interview preparation as a learning graph.
When a user fails a problem, the system intelligently recommends conceptually similar and easier problems to reinforce understanding.

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Link to product walkthrough

Check out the live demo of the project here.

Architecture

Frontend (HTML / CSS / Vanilla JavaScript)
        ↓
Controller Layer (Spring Boot REST APIs)
        ↓
Service Layer (Business Logic)
        ↓
DSA Layer
 ├── Graph (Adjacency List + BFS Traversal)
 ├── Trie (Prefix-based Tag Indexing)
 ├── Heap (PriorityQueue-based Ranking)
        ↓
In-Memory Storage (HashMaps)

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Core Data Structures Used

1. Graph (Adjacency List)

• Models problems as nodes
• Edges represent conceptual similarity and difficulty progression
• BFS traversal recommends nearby learning problems

2. Trie

• Stores problem tags
• Supports prefix-based tag search in O(L)
• Faster than scanning all tags

3. Heap (PriorityQueue)

• Dynamically ranks problems
• Prioritizes:
  • Higher failure count
  • Lower difficulty
  • Lower average solve time

4. HashMap

• Stores problems and attempts in memory
• Enables O(1) access

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Recommendation Flow (Example)

User fails "3Sum"
      ↓
Failure recorded (Performance Engine)
      ↓
Graph BFS finds related problems
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Filter problems with same or lower difficulty
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Heap ranks based on failure frequency
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Top-K recommendations returned

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REST APIs

Method	Endpoint	Description
GET	/problems	Fetch all problems
POST	/attempt	Record solve/fail attempt
GET	/recommend	Fetch recommendations
GET	/stats	Fetch performance stats

All APIs return JSON.

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Time & Space Complexity

Component	Complexity
Graph BFS	O(V + E)
Trie Prefix Search	O(L)
Heap Insertion	O(log N)
Heap Top-K	O(K log N)
HashMap Access	O(1)

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Future Improvements

• Replace aggregated metrics with Segment Tree
• Persist data using database
• Add user authentication
• Difficulty heatmap & progress visualization
• Adaptive learning paths using ML

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Tech Stack

• Backend: Java, Spring Boot
• Frontend: HTML, CSS, Vanilla JavaScript
• Storage: In-memory data structures
• Deployment: Single Spring Boot JAR (Render / Railway compatible)

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Key Takeaway

InterviewOS demonstrates:

• Strong DSA fundamentals
• Clean system design
• Scalable thinking
• Interview-ready backend engineering