# Oreo: Detection of Clones in the Twilight Zone

arXiv: https://arxiv.org/abs/1806.05837 · HTML: https://mlsanity.com/paper/1806.05837/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2018.

## Built on by (most cited first, 19 of 19)

- [Trustworthy and Synergistic Artificial Intelligence for Software Engineering: Vision and Roadmaps](https://mlsanity.com/paper/2309.04142/) (2023)
- [A systematic literature review on source code similarity measurement and clone detection: techniques, applications, and challenges](https://mlsanity.com/paper/2306.16171/) (2023)
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- [Generalizability of Code Clone Detection on CodeBERT](https://mlsanity.com/paper/2208.12588/) (2022)
- [TransformCode: A Contrastive Learning Framework for Code Embedding via Subtree Transformation](https://mlsanity.com/paper/2311.08157/) (2023)
- [Senatus -- A Fast and Accurate Code-to-Code Recommendation Engine](https://mlsanity.com/paper/2111.04473/) (2021)
- [Unveiling the potential of large language models in generating semantic and cross-language clones](https://mlsanity.com/paper/2309.06424/) (2023)
- [The Struggles of LLMs in Cross-lingual Code Clone Detection](https://mlsanity.com/paper/2408.04430/) (2024)
- [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)
- [Assessing the Code Clone Detection Capability of Large Language Models](https://mlsanity.com/paper/2407.02402/) (2024)
- [Call Me Maybe: Enhancing JavaScript Call Graph Construction using Graph Neural Networks](https://mlsanity.com/paper/2506.18191/) (2025)
- [Previously on... Automating Code Review](https://mlsanity.com/paper/2508.18003/) (2025)
- [Borrowing from Similar Code: A Deep Learning NLP-Based Approach for Log Statement Automation](https://mlsanity.com/paper/2112.01259/) (2021)
- [Selecting and Combining Large Language Models for Scalable Code Clone Detection](https://mlsanity.com/paper/2510.15480/) (2025)
- [Given, When, Then, Again: Mining Subscenario Refactoring Candidates in Behaviour-Driven Test Suites with ML Classifiers and LLM-Judge Baselines](https://mlsanity.com/paper/2605.14568/) (2026)
- [Super Library Agent: Joint Generation and Maintenance of Multiple Applications Beyond the Single Codebase](https://mlsanity.com/paper/2608.29310/) (2026)

## Abstract (arXiv)

Source code clones are categorized into four types of increasing difficulty of detection, ranging from purely textual (Type-1) to purely semantic (Type-4). Most clone detectors reported in the literature work well up to Type-3, which accounts for syntactic differences. In between Type-3 and Type-4, however, there lies a spectrum of clones that, although still exhibiting some syntactic similarities, are extremely hard to detect -- the Twilight Zone. Most clone detectors reported in the literature fail to operate in this zone. We present Oreo, a novel approach to source code clone detection that not only detects Type-1 to Type-3 clones accurately, but is also capable of detecting harder-to-detect clones in the Twilight Zone. Oreo is built using a combination of machine learning, information retrieval, and software metrics. We evaluate the recall of Oreo on BigCloneBench, and perform manual evaluation for precision. Oreo has both high recall and precision. More importantly, it pushes the boundary in detection of clones with moderate to weak syntactic similarity in a scalable manner.