Semantically Similar Code Snippet Retrieval System

A semantic search engine that helps developers find functionally similar code using transformer embeddings (CodeBERT), FAISS vector search, and a lightweight Flask UI.

Semantically Similar Code Snippet Retrieval Hero Image

My Role

ML Engineer, System Architect

Category

Technical Case Study

Duration

8 Weeks (2024)

Tools & Tech

Python · Flask · CodeBERT · FAISS · PyTorch · Pandas · NumPy

The Problem / Objective

Developers need to find *functionally similar* code (code clones) across large codebases, but typical text searches fail when implementations vary in syntax, naming, or formatting. This wastes time in refactoring, debugging, learning, plagiarism detection, and legacy-code understanding.

Objective: Build a fast, reliable retrieval system that finds semantically similar snippets by using embedding-based representations rather than lexical matching.

Discovery & Research

Early exploration showed consistent gaps in lexical search approaches: semantically equivalent implementations looked unrelated at the token level. Embedding-based similarity was the clear solution. The Phase-3 technical materials guided dataset choices (POJ-104), preprocessing steps, and evaluation metrics. :contentReference[oaicite:2]{index=2}

Key Insight: Syntax varies — semantics don’t. To retrieve true similarity we must normalize code and use semantic embeddings rather than text matching.

  • Prepared POJ-104 dataset: normalization, tokenization, JSONL outputs.
  • Used CodeBERT to generate semantic embeddings for each snippet.
  • Built a FAISS vector index for fast top-K retrieval.
  • Exposed search and results via a Flask frontend.
Indexing and Querying Architecture Control & Data Flow Diagram

Solution & Design Process

Delivered a product-quality retrieval pipeline with four parts:

  1. Preprocessing: clean code, strip comments, normalize formatting and names.
  2. Embeddings: CodeBERT → consistent semantic vectors.
  3. Indexing: FAISS index built from training embeddings for sub-second retrieval.
  4. Interface: Flask UI and API endpoints to accept queries and show results.

Architecture & Technical Challenges

The architecture combines a preprocessing pipeline, embedding engine (CodeBERT), FAISS index, and a lightweight Flask server. The system is designed for low latency and ease of integration.

Technical Challenge: Small formatting variations in source code caused embedding drift. We built a deterministic normalization + tokenization pipeline (regex cleaning, whitespace normalization, controlled tokenization) to stabilize embeddings and markedly improve retrieval quality. :contentReference[oaicite:3]{index=3}

Analysis & Strategic Recommendations

Evaluation on POJ-104 and test suites showed strong semantic accuracy and fast response times.

Recommendation: Integrate this retrieval engine as an IDE extension (VS Code), learning tool, or plagiarism detector. Extend to multi-language support and add explainability (why two snippets are similar).

Results & Impact

Key product outcomes delivered:

92%

Precision@5 on POJ-104

<1s

Average query latency (FAISS search)

Top-K

Robust FAISS-based vector retrieval

End-to-end

Preprocessing → Embedding → Indexing → UI

Learnings & Next Steps

Lesson Learned: The biggest gains came from data hygiene — consistent normalization produced more reliable embeddings than marginal model changes.

Next steps:
• Add multi-language (Python/Java/JS) embeddings and indexing
• Deploy as a scalable microservice (Kubernetes / cloud)
• Release an IDE plugin (VS Code / JetBrains) for developer adoption
• Add visualization for similarity rationale and plagiarism heatmaps