A Survey on the Evaluation of Clone Detection Performance and Benchmarking

Svajlenko, Jeffrey; Roy, Chanchal K.

doi:10.48550/arxiv.2006.15682

Cited by 2 publications

(3 citation statements)

References 138 publications

(317 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Roy et al [6] focused on clone detection techniques and tools, providing a concise but comprehensive survey and a hypothetical evaluation based on editing scenarios. Recently, this work was extended by Svajlenko and Roy [13] to include more clone detection tools. Amro [14] summarized most of the malware detection techniques used by Android and iOS, and he presented a brief description of each technique.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Exploring Android Obfuscators and Deobfuscators: An Empirical Investigation

Ebad,

Darem

2024

Electronics

View full text Add to dashboard Cite

Researchers have proposed different obfuscation transformations supported by numerous smartphone protection tools (obfuscators and deobfuscators). However, there is a need for a comprehensive study to empirically characterize these tools that belong to different categories of transformations. We propose a property-based framework to systematically classify twenty cutting-edge tools according to their features, analysis type, programming language support, licensing, applied obfuscation transformations, and general technical drawbacks. Our analysis predominantly reveals that very few tools work at the dynamic level, and most tools (which are static-based) work for Java or Java-based ecosystems (e.g., Android). The findings also show that the widespread adoption of renaming transformations is followed by formatting and code injection. In addition, this paper pinpoints the technical shortcomings of each tool; some of these drawbacks are common in static-based analyzers (e.g., resource consumption), and other drawbacks have negative effects on the experiment conducted by students (e.g., a third-party library involved). According to these critical limitations, we provide some timely recommendations for further research. This study can assist not only Android developers and researchers to improve the overall health of their apps but also the managers of computer science and cybersecurity academic programs to embed suitable obfuscation tools in their curricula.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Most attempts have focused on a small number of obfuscators [1,11,12,[14][15][16]. Others narrowed their research to specific types of obfuscators, such as plagiarism/cloning [6,13,17], network protocols [10], or JavaScript [1]. The rest summarized some of the obfuscation transformations with no mention of tools [14].…”

Section: Related Workmentioning

confidence: 99%

Exploring Android Obfuscators and Deobfuscators: An Empirical Investigation

Ebad,

Darem

2024

Electronics

View full text Add to dashboard Cite

show abstract

“…An important case of code similarity detection is the detection of code clones, i.e., code snippets with identical functionality. Code clones are commonly divided into four types [52], with Type IV being the hardest to detect. Type I-III clones refer to gradually increasing differences in the implementation (e.g., changed names of tokens, order of operations, insertion of dead code).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Contrastive Learning with Various Code Representations for Code Clone Detection

Zubkov¹,

Spirin²,

Bogomolov³

et al. 2022

Preprint

View full text Add to dashboard Cite

Code clones are pairs of code snippets that implement similar functionality. Clone detection is a fundamental branch of automatic source code comprehension, having many applications in refactoring recommendation, plagiarism detection, and code summarization. A particularly interesting case of clone detection is the detection of semantic clones, i.e., code snippets that have the same functionality but significantly differ in implementation. A promising approach to detecting semantic clones is contrastive learning (CL), a machine learning paradigm popular in computer vision but not yet commonly adopted for code processing.Our work aims to evaluate the most popular CL algorithms combined with three source code representations on two tasks. The first task is code clone detection, which we evaluate on the POJ-104 dataset containing implementations of 104 algorithms. The second task is plagiarism detection. To evaluate the models on this task, we introduce CodeTransformator, a tool for transforming source code. We use it to create a dataset that mimics plagiarised code based on competitive programming solutions. We trained nine models for both tasks and compared them with six existing approaches, including traditional tools and modern pre-trained neural models. The results of our evaluation show that proposed models perform diversely in each task, however the performance of the graph-based models is generally above the others. Among CL algorithms, SimCLR and SwAV lead to better results, while Moco is the most robust approach. Our code and trained models are available at

show abstract

A Survey on the Evaluation of Clone Detection Performance and Benchmarking

Cited by 2 publications

References 138 publications

Exploring Android Obfuscators and Deobfuscators: An Empirical Investigation

Exploring Android Obfuscators and Deobfuscators: An Empirical Investigation

Evaluation of Contrastive Learning with Various Code Representations for Code Clone Detection

Contact Info

Product

Resources

About