# A systematic literature review on source code similarity measurement and clone detection: techniques, applications, and challenges

arXiv: https://arxiv.org/abs/2306.16171 · HTML: https://mlsanity.com/paper/2306.16171/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 256 references that are in the map.
- First posted on arXiv 28 Jun 2023.

## Builds on (most cited first, 28 of 28)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021)
- [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020)
- [code2vec: Learning Distributed Representations of Code](https://mlsanity.com/paper/1803.09473/) (2018)
- [Graph Matching Networks for Learning the Similarity of Graph Structured Objects](https://mlsanity.com/paper/1904.12787/) (2019)
- [code2seq: Generating Sequences from Structured Representations of Code](https://mlsanity.com/paper/1808.01400/) (2018)
- [A Convolutional Attention Network for Extreme Summarization of Source Code](https://mlsanity.com/paper/1602.03001/) (2016)
- [DOBF: A Deobfuscation Pre-Training Objective for Programming Languages](https://mlsanity.com/paper/2102.07492/) (2021)
- [SourcererCC: Scaling Code Clone Detection to Big Code](https://mlsanity.com/paper/1512.06448/) (2015)
- [Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities](https://mlsanity.com/paper/1707.04742/) (2017)
- [InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees](https://mlsanity.com/paper/2012.07023/) (2020)
- [Oreo: Detection of Clones in the Twilight Zone](https://mlsanity.com/paper/1806.05837/) (2018)
- [Modeling Functional Similarity in Source Code with Graph-Based Siamese Networks](https://mlsanity.com/paper/2011.11228/) (2020)
- [Unleashing the Power of Compiler Intermediate Representation to Enhance Neural Program Embeddings](https://mlsanity.com/paper/2204.09191/) (2022)
- [The Effectiveness of Supervised Machine Learning Algorithms in Predicting Software Refactoring](https://mlsanity.com/paper/2001.03338/) (2020)
- [An ensemble meta-estimator to predict source code testability](https://mlsanity.com/paper/2208.09614/) (2022)
- [A Machine Learning Based Framework for Code Clone Validation](https://mlsanity.com/paper/2005.00967/) (2020)
- [DeepClone: Modeling Clones to Generate Code Predictions](https://mlsanity.com/paper/2007.11671/) (2020)
- [A systematic literature review on the code smells datasets and validation mechanisms](https://mlsanity.com/paper/2306.01377/) (2023)
- [Mercem: Method Name Recommendation Based on Call Graph Embedding](https://mlsanity.com/paper/1907.05690/) (2019)
- [LVMapper: A Large-variance Clone Detector Using Sequencing Alignment Approach](https://mlsanity.com/paper/1909.04238/) (2019)
- [Clone-Seeker: Effective Code Clone Search Using Annotations](https://mlsanity.com/paper/2106.03042/) (2021)
- [Learning to predict test effectiveness](https://mlsanity.com/paper/2208.09623/) (2022)
- [SourcererCC and SourcererCC-I: Tools to Detect Clones in Batch mode and During Software Development](https://mlsanity.com/paper/1603.01661/) (2016)
- [Semantic Clone Detection via Probabilistic Software Modeling](https://mlsanity.com/paper/2008.04891/) (2020)
- [Integrated Reasoning Engine for Pointer-related Code Clone Detection](https://mlsanity.com/paper/2105.11933/) (2021)
- [SEED: Semantic Graph based Deep detection for type-4 clone](https://mlsanity.com/paper/2109.12079/) (2021)

## Built on by (most cited first, 14 of 14)

- [LessLeak-Bench: A First Investigation of Data Leakage in LLMs Across 83 Software Engineering Benchmarks](https://mlsanity.com/paper/2502.06215/) (2025)
- [LiCoEval: Evaluating LLMs on License Compliance in Code Generation](https://mlsanity.com/paper/2408.02487/) (2024)
- [Laurel: Unblocking Automated Verification with Large Language Models](https://mlsanity.com/paper/2405.16792/) (2024)
- [A Large-Scale Study of Model Integration in ML-Enabled Software Systems](https://mlsanity.com/paper/2408.06226/) (2024)
- [Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Unified Approach for Elevating Benchmark Quality](https://mlsanity.com/paper/2503.05860/) (2025)
- [Unraveling Code Clone Dynamics in Deep Learning Frameworks](https://mlsanity.com/paper/2404.17046/) (2024)
- [AlgoSimBench: Identifying Algorithmically Similar Problems for Competitive Programming](https://mlsanity.com/paper/2507.15378/) (2025)
- [Integrating Large Language Models and Reinforcement Learning for Non-Linear Reasoning](https://mlsanity.com/paper/2410.13501/) (2024)
- [On the Use of Deep Learning Models for Semantic Clone Detection](https://mlsanity.com/paper/2412.14739/) (2024)
- [TRACER: A Semantic-Aware Framework for Fine-Grained Contamination Detection in Code LLMs](https://mlsanity.com/paper/2605.24079/) (2026)
- [Automated Code Generation and Validation for Software Components of Microcontrollers](https://mlsanity.com/paper/2502.18905/) (2025)
- [Evaluating LLMs for One-Shot Patching of Real and Artificial Vulnerabilities](https://mlsanity.com/paper/2511.23408/) (2025)
- [Can Code Evaluation Metrics Detect Code Plagiarism?](https://mlsanity.com/paper/2604.25778/) (2026)
- [Type-IV Code Clone Detection via Layer-Wise Non-Contrastive Representation Learning](https://mlsanity.com/paper/2609.17338/) (2026)

## Abstract (arXiv)

Measuring and evaluating source code similarity is a fundamental software engineering activity that embraces a broad range of applications, including but not limited to code recommendation, duplicate code, plagiarism, malware, and smell detection. This paper proposes a systematic literature review and meta-analysis on code similarity measurement and evaluation techniques to shed light on the existing approaches and their characteristics in different applications. We initially found over 10000 articles by querying four digital libraries and ended up with 136 primary studies in the field. The studies were classified according to their methodology, programming languages, datasets, tools, and applications. A deep investigation reveals 80 software tools, working with eight different techniques on five application domains. Nearly 49% of the tools work on Java programs and 37% support C and C++, while there is no support for many programming languages. A noteworthy point was the existence of 12 datasets related to source code similarity measurement and duplicate codes, of which only eight datasets were publicly accessible. The lack of reliable datasets, empirical evaluations, hybrid methods, and focuses on multi-paradigm languages are the main challenges in the field. Emerging applications of code similarity measurement concentrate on the development phase in addition to the maintenance.