# SourcererCC: Scaling Code Clone Detection to Big Code

arXiv: https://arxiv.org/abs/1512.06448 · HTML: https://mlsanity.com/paper/1512.06448/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2015.

## Built on by (most cited first, 30 of 40)

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- [Is Functional Correctness Enough to Evaluate Code Language Models? Exploring Diversity of Generated Codes](https://mlsanity.com/paper/2408.14504/) (2024)
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- [Senatus -- A Fast and Accurate Code-to-Code Recommendation Engine](https://mlsanity.com/paper/2111.04473/) (2021)
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- [REINFOREST: Reinforcing Semantic Code Similarity for Cross-Lingual Code Search Models](https://mlsanity.com/paper/2305.03843/) (2023)
- [ZC3: Zero-Shot Cross-Language Code Clone Detection](https://mlsanity.com/paper/2308.13754/) (2023)
- [The Struggles of LLMs in Cross-lingual Code Clone Detection](https://mlsanity.com/paper/2408.04430/) (2024)
- [JEMMA: An Extensible Java Dataset for ML4Code Applications](https://mlsanity.com/paper/2212.09132/) (2022)
- [xASTNN: Improved Code Representations for Industrial Practice](https://mlsanity.com/paper/2303.07104/) (2023)
- [Unraveling Code Clone Dynamics in Deep Learning Frameworks](https://mlsanity.com/paper/2404.17046/) (2024)
- [Evaluating Tool Cloning in Agentic-AI Ecosystems](https://mlsanity.com/paper/2605.09817/) (2026)
- [Leveraging Code Clones and Natural Language Processing for Log Statement Prediction](https://mlsanity.com/paper/2109.03859/) (2021)
- [Code Clone Detection based on Event Embedding and Event Dependency](https://mlsanity.com/paper/2111.14183/) (2021)
- [Beyond Self-learned Attention: Mitigating Attention Bias in Transformer-based Models Using Attention Guidance](https://mlsanity.com/paper/2402.16790/) (2024)
- [AdaptEval: A Benchmark for Evaluating Large Language Models on Code Snippet Adaptation](https://mlsanity.com/paper/2601.04540/) (2026)
- [TRACER: A Semantic-Aware Framework for Fine-Grained Contamination Detection in Code LLMs](https://mlsanity.com/paper/2605.24079/) (2026)
- [Borrowing from Similar Code: A Deep Learning NLP-Based Approach for Log Statement Automation](https://mlsanity.com/paper/2112.01259/) (2021)
- [Understanding Long Programming Languages with Structure-Aware Sparse Attention](https://mlsanity.com/paper/2205.13730/) (2022)
- [Sparse Attention-Based Neural Networks for Code Classification](https://mlsanity.com/paper/2311.06575/) (2023)

## Abstract (arXiv)

Despite a decade of active research, there is a marked lack in clone detectors that scale to very large repositories of source code, in particular for detecting near-miss clones where significant editing activities may take place in the cloned code. We present SourcererCC, a token-based clone detector that targets three clone types, and exploits an index to achieve scalability to large inter-project repositories using a standard workstation. SourcererCC uses an optimized inverted-index to quickly query the potential clones of a given code block. Filtering heuristics based on token ordering are used to significantly reduce the size of the index, the number of code-block comparisons needed to detect the clones, as well as the number of required token-comparisons needed to judge a potential clone. We evaluate the scalability, execution time, recall and precision of SourcererCC, and compare it to four publicly available and state-of-the-art tools. To measure recall, we use two recent benchmarks, (1) a large benchmark of real clones, BigCloneBench, and (2) a Mutation/Injection-based framework of thousands of fine-grained artificial clones. We find SourcererCC has both high recall and precision, and is able to scale to a large inter-project repository (250MLOC) using a standard workstation.