Source Code Clone Detection Using Unsupervised Similarity Measures

This repository contains the source code for reproducing the paper Martinez-Gil, J. (2024). Source Code Clone Detection Using Unsupervised Similarity Measures. In: Bludau, P., Ramler, R., Winkler, D., Bergsmann, J. (eds) Software Quality as a Foundation for Security. SWQD 2024. Lecture Notes in Business Information Processing, vol 505. Springer, Cham. https://doi.org/10.1007/978-3-031-56281-5_2.

The dataset has been created by Oscar Karnalim: https://github.com/oscarkarnalim/sourcecodeplagiarismdataset

🌍 Overview

This project implements a collection of established methods for measuring similarity. In this context, the goal is to detect similarity (and subsequently identify code clones) in Java source code using unsupervised similarity measures. It aims to check the most promising unsupervised similarity measures to identify duplicates (a.k.a. clones) in source code efficiently, offering a valuable tool for software maintenance and plagiarism detection.

📚 Reference

If you use this work, please cite:

@InProceedings{10.1007/978-3-031-56281-5_2,
	author="Martinez-Gil, Jorge",
	editor="Bludau, Peter
	and Ramler, Rudolf
	and Winkler, Dietmar
	and Bergsmann, Johannes",
	title="Source Code Clone Detection Using Unsupervised Similarity Measures",
	booktitle="Software Quality as a Foundation for Security",
	year="2024",
	publisher="Springer Nature Switzerland",
	address="Cham",
	pages="21--37",
	isbn="978-3-031-56281-5"
}

🛠️ How it Works

Each script java-sim-*-opt.py processes the Java code snippets from the IR-Plag dataset. There are 21 different methods implemented. Please note that each script tries to find a threshold value for semantic similarity capable of separating clones from non-clones.

📈 Performance Results

Approach	Script	Accuracy	Precision	Recall	F-Measure	Execution Time (ms)
Abstract Syntax Tree	java-sim-ast-opt.py	0.77	0.77	0.78	0.78	80907.37
Bag-of-Words	java-sim-bow-opt.py	0.77	0.79	0.66	0.72	57444.90
Bag-of-Words II	java-sim-bow2-opt.py	0.77	0.77	1.00	0.87	59961.69
CodeBERT	java-sim-codebert-opt.py*	0.54	0.75	0.34	0.47	868755.96
Comment Sim.	java-sim-comments-opt.py	0.77	0.77	1.00	0.87	983231.42
Output Analysis	java-sim-exec-opt.py	0.94	0.85	0.97	0.90	1381335.16
Function Calls	java-sim-fcall-opt.py	0.78	0.78	0.91	0.84	30303.88
Fuzzy Matching	java-sim-fuzz-opt.py	0.77	0.77	1.00	0.87	12778.62
Graph Matching	java-sim-graph-opt.py	0.78	0.80	0.52	0.63	65076.91
Rolling Hash	java-sim-hash-opt.py	0.59	0.93	0.18	0.30	959157.60
Perceptual Hash	java-sim-image-opt.py	0.77	0.77	0.85	0.81	38152.71
Jaccard	java-sim-jaccard-opt.py	0.86	0.81	0.94	0.87	2066.13
Longest Common Subsequence	java-sim-lcs-opt.py	0.48	0.74	0.06	0.11	7268.67
Levenshtein	java-sim-lev-opt.py	0.77	0.80	0.66	0.72	10280.09
Metrics comparison	java-sim-metrics-opt.py	0.77	0.77	1.00	0.87	60508.62
N-Grams	java-sim-ngrams-opt.py	0.85	0.84	0.29	0.43	66635.25
Program Dependence Graph	java-sim-pdg-opt.py	0.65	0.85	0.39	0.53	40518.80
Rabin-Karp	java-sim-rk-opt.py	0.81	0.79	0.99	0.88	225218.76
Semantic Clone	java-sim-semclone-opt.py	0.77	0.79	0.68	0.73	41543.53
Semdiff method	java-sim-semdiff-opt.py	0.77	0.79	0.38	0.51	26351.06
TDF-IDF	java-sim-tdf-opt.py	0.77	0.77	0.99	0.87	68587.17
Winnow	java-sim-winn-opt.py	0.86	0.81	0.98	0.88	77160.81
Winnow II	java-sim-winn2-opt.py	0.83	0.80	0.94	0.87	104032.99

*CodeBERT is used without recalibration

📄 License

These scripts are provided under the MIT License.