# On the Use of Deep Learning Models for Semantic Clone Detection

arXiv: https://arxiv.org/abs/2412.14739 · HTML: https://mlsanity.com/paper/2412.14739/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 34 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Builds on (most cited first, 13 of 13)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021)
- [Graph Matching Networks for Learning the Similarity of Graph Structured Objects](https://mlsanity.com/paper/1904.12787/) (2019)
- [The Science of Detecting LLM-Generated Texts](https://mlsanity.com/paper/2303.07205/) (2023)
- [Analyzing and Improving Representations with the Soft Nearest Neighbor Loss](https://mlsanity.com/paper/1902.01889/) (2019)
- [A systematic literature review on source code similarity measurement and clone detection: techniques, applications, and challenges](https://mlsanity.com/paper/2306.16171/) (2023)
- [Cross-Language Code Search using Static and Dynamic Analyses](https://mlsanity.com/paper/2106.09173/) (2021)
- [SLACC: Simion-based Language Agnostic Code Clones](https://mlsanity.com/paper/2002.03039/) (2020)
- [Unveiling the potential of large language models in generating semantic and cross-language clones](https://mlsanity.com/paper/2309.06424/) (2023)
- [Evaluating the Performance of Clone Detection Tools in Detecting Cloned Co-change Candidates](https://mlsanity.com/paper/2201.07996/) (2022)

## Built on by (most cited first, 1 of 1)

- [Neuron-Guided Interpretation of Code LLMs: Where, Why, and How?](https://mlsanity.com/paper/2512.19980/) (2025)

## Abstract (arXiv)

Detecting and tracking code clones can ease various software development and maintenance tasks when changes in a code fragment should be propagated over all its copies. Several deep learning-based clone detection models have appeared in the literature for detecting syntactic and semantic clones, widely evaluated with the BigCloneBench dataset. However, class imbalance and the small number of semantic clones make BigCloneBench less ideal for interpreting model performance. Researchers also use other datasets such as GoogleCodeJam, OJClone, and SemanticCloneBench to understand model generalizability. To overcome the limitations of existing datasets, the GPT-assisted semantic and cross-language clone dataset GPTCloneBench has been released. However, how these models compare across datasets remains unclear. In this paper, we propose a multi-step evaluation approach for five state-of-the-art clone detection models leveraging existing benchmark datasets, including GPTCloneBench, and using mutation operators to study model ability. Specifically, we examine three highly-performing single-language models (ASTNN, GMN, CodeBERT) on BigCloneBench, SemanticCloneBench, and GPTCloneBench, testing their robustness with mutation operations. Additionally, we compare them against cross-language models (C4, CLCDSA) known for detecting semantic clones. While single-language models show high F1 scores for BigCloneBench, their performance on SemanticCloneBench varies (up to 20%). Interestingly, the cross-language model (C4) shows superior performance (around 7%) on SemanticCloneBench over other models and performs similarly on BigCloneBench and GPTCloneBench. On mutation-based datasets, C4 has more robust performance (less than 1% difference) compared to single-language models, which show high variability.