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Evaluation of Code Similarity Search Strategies in Large-Scale Codebases

DOI Springer License Python GitHub Stars

TL;DR — We systematically benchmark 6 approximate nearest-neighbour engines (Annoy, Elasticsearch, FAISS, HNSWlib, ScaNN, scikit-learn) paired with both TF-IDF and CodeBERT embeddings on the BigCloneBench dataset. Key finding: FAISS offers the best scalability; CodeBERT gives the highest semantic accuracy; Elasticsearch leads on raw query speed.

📖 Abstract

This repository accompanies the Springer TLDKS LVII paper Evaluation of Code Similarity Search Strategies in Large-Scale Codebases and provides reproducible scripts for benchmarking source-code retrieval pipelines at scale. We evaluate lexical and semantic representations (TF-IDF and CodeBERT) combined with multiple ANN/vector-search engines and report indexing cost, query efficiency, and practical trade-offs for large software corpora.

🏗️ Architecture

Raw JSONL ──► Vectorization ──► Index Construction ──► Query Benchmarking ──► Results & Plots
                (TF-IDF /           (FAISS / Annoy /        (latency, throughput,
                CodeBERT)           HNSW / SKLNN /           relevance metrics)
                                    Elasticsearch /
                                    ScaNN)

🛠️ Methods Evaluated

Method Type Description
Annoy Tree-based Approximate nearest-neighbor search with tree-based partitioning.
Elasticsearch Inverted Index Vector and text-based search using tunable scoring.
FAISS Clustering/Quantization Facebook AI Similarity Search; efficient high-dimensional search.
HNSW Graph-based Hierarchical Navigable Small World graphs.
ScaNN Quantization Google's Scalable Nearest Neighbors with partitioning.
SKLNN Brute/Tree scikit-learn nearest-neighbor algorithms.

📊 Key Results

Finding Summary
Semantic quality CodeBERT improves semantic relevance versus TF-IDF across backends.
Query speed Elasticsearch provides the best raw query latency in the reported experiments.
Scalability FAISS offers the strongest scalability profile for very large indexes.
Simplicity scikit-learn is a strong baseline for small/medium datasets.

📂 Repository Structure

.
├── .github/workflows/ci.yml            # CI smoke tests and optional pytest
├── all.py                              # End-to-end benchmark + plots
├── indexing.py                         # Indexing-time benchmark
├── performance.py                      # Latency/QPS benchmark
├── plots.py                            # Benchmark plotting utility
├── testcodebert.py                     # TF-IDF vs CodeBERT benchmark
├── examples/quickstart.py              # Self-contained quickstart demo
├── src/similarity_search/utils.py      # Shared benchmark/data helpers
├── tests/test_utils.py                 # Unit tests for shared helpers
├── docs/ARCHITECTURE.md                # System architecture details
├── docs/RESULTS.md                     # Reproducible results summary
├── requirements.txt                    # Reproducible runtime dependencies
├── pyproject.toml                      # Modern packaging metadata
├── CONTRIBUTING.md                     # Contribution guide
└── CHANGELOG.md                        # Release history

⚡ Quick Start

python -m pip install -r requirements.txt
python examples/quickstart.py

🚀 Full Reproducibility Guide

Prerequisites

  • Python 3.8+
  • BigCloneBench JSONL data (CodeXGLUE format)
  • Optional: Elasticsearch 8.x for Elasticsearch experiments

Installation

pip install -e .
# or
pip install -r requirements.txt

Dataset Setup

  1. Download BigCloneBench from CodeXGLUE.
  2. Place the file at data/data.jsonl.
  3. Ensure each line has JSON with a func field.

Running Experiments

python all.py --data data/data.jsonl --k 3 --trials 100
python indexing.py --data data/data.jsonl --sizes 100 1000 10000
python performance.py --data data/data.jsonl --query "for(int i=0;i<n;i++){sum+=i;}"
python plots.py --data data/data.jsonl --time-plot search_time_comparison.tex
python testcodebert.py --data data/data.jsonl --trials 100

Elasticsearch Setup

docker run -p 9200:9200 -e discovery.type=single-node -e xpack.security.enabled=false docker.elastic.co/elasticsearch/elasticsearch:8.14.0

For authenticated setups, copy .env.example to .env and configure ES_HOST, ES_USER, and ES_PASSWORD.

🔬 Extending the Benchmark

  1. Add backend-specific index/search functions in benchmark scripts.
  2. Reuse read_code_snippets and benchmark_search from src/similarity_search/utils.py.
  3. Add reproducible output and update docs/results tables.
  4. Include tests for any shared utilities.

📝 Citation

@incollection{martinez2024evaluation,
  title={Evaluation of Code Similarity Search Strategies in Large-Scale Codebases},
  author={Martinez-Gil, Jorge and Yin, Shaoyi},
  booktitle={Transactions on Large-Scale Data-and Knowledge-Centered Systems LVII},
  pages={99--113},
  year={2024},
  publisher={Springer}
}

🤝 Contributing

See CONTRIBUTING.md.

📄 License

MIT. See LICENSE.