# Source Code Clone Detection Using Unsupervised Similarity Measures

arXiv: https://arxiv.org/abs/2401.09885 · HTML: https://mlsanity.com/paper/2401.09885/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2024.

## Built on by (most cited first, 5 of 5)

- [CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval](https://mlsanity.com/paper/2411.12644/) (2024)
- [Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets](https://mlsanity.com/paper/2503.17502/) (2025)
- [Functional Consistency of LLM Code Embeddings: A Self-Evolving Data Synthesis Framework for Benchmarking](https://mlsanity.com/paper/2508.19558/) (2025)
- [Simulation Code Generation for Fluid Systems using Large Language Models: Benchmarking Models and Prompting Strategies](https://mlsanity.com/paper/2607.29389/) (2026)
- [Advanced Detection of Source Code Clones via an Ensemble of Unsupervised Similarity Measures](https://mlsanity.com/paper/2405.02095/) (2024)

## Abstract (arXiv)

Assessing similarity in source code has gained significant attention in recent years due to its importance in software engineering tasks such as clone detection and code search and recommendation. This work presents a comparative analysis of unsupervised similarity measures for identifying source code clone detection. The goal is to overview the current state-of-the-art techniques, their strengths, and weaknesses. To do that, we compile the existing unsupervised strategies and evaluate their performance on a benchmark dataset to guide software engineers in selecting appropriate methods for their specific use cases. The source code of this study is available at https://github.com/jorge-martinez-gil/codesim