Detection and Classification of Cross-language Code Clone Types by Filtering the Nodes of ANTLR-generated Parse Tree

Ankali, Sanjay B.; Parthiban, Latha

doi:10.5815/ijisa.2021.03.05

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…The AST uses ANTLR parser to evaluate the abstract view of each document in a hierarchical fashion for different types of codes, such as Java, C#, and C++. The ANTLR 34 is an effective parser producer which can read, examine, execute, and interpret hierarchical programming codes. It is often used to build parsers that can traverse syntax trees.…”

Section: Proposed Method: Cross‐language Oss Classificationmentioning

confidence: 99%

CroLSSim: Cross‐language software similarity detector using hybrid approach of LSA‐based AST‐MDrep features and CNN‐LSTM model

Ullah

Naeem

et al. 2022

Int J of Intelligent Sys

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Software similarity in different programming codes is a rapidly evolving field because of its numerous applications in software development, software cloning, software plagiarism, and software forensics. Currently, software researchers and developers search cross‐language open‐source repositories for similar applications for a variety of reasons, such as reusing programming code, analyzing different implementations, and looking for a better application. However, it is a challenging task because each programming language has a unique syntax and semantic structure. In this paper, a novel tool called Cross‐Language Software Similarity (CroLSSim) is designed to detect similar software applications written in different programming codes. First, the Abstract Syntax Tree (AST) features are collected from different programming codes. These are high‐quality features that can show the abstract view of each program. Then, Methods Description (MDrep) in combination with AST is used to examine the relationship among different method calls. Second, the Term Frequency Inverse Document Frequency approach is used to retrieve the local and global weights from AST‐MDrep features. Third, the Latent Semantic Analysis‐based features extraction and selection method is proposed to extract the semantic anchors in reduced dimensional space. Fourth, the Convolution Neural Network (CNN)‐based features extraction method is proposed to mine the deep features. Finally, a hybrid deep learning model of CNN‐Long‐Short‐Term Memory is designed to detect semantically similar software applications from these latent variables. The data set contains approximately 9.5K Java, 8.8K C#, and 7.4K C++ software applications obtained from GitHub. The proposed approach outperforms as compared with the state‐of‐the‐art methods.

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Section: Proposed Method: Cross‐language Oss Classificationmentioning

confidence: 99%

CroLSSim: Cross‐language software similarity detector using hybrid approach of LSA‐based AST‐MDrep features and CNN‐LSTM model

Ullah

Naeem

et al. 2022

Int J of Intelligent Sys

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show abstract

“…The inherent dissimilarities stemming from language-specific grammatical nuances render token-based and textbased traditional approaches less effective for cross-language clone detection [24]. Recent advancements in deep learning models have demonstrated commendable performance in detecting code clones within single-language contexts [25], [26], [27]. These models exhibit a notable ability to harness the underlying structure and semantics of code fragments, facilitating the identification of code clones.…”

Section: Introductionmentioning

confidence: 99%

Transcompiler CNN-GMN Based Cross Language Clone Detection and Algorithm Recognition

Singh,

Bhutiya,

Goyal

et al. 2023

IJRASET

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Software replication stemming from code reutilization introduces complexities in software maintenance. While deep learning-driven clone detection tools abound, they predominantly cater to singular programming languages. Concurrently, the task of identifying algorithms within program code remains challenging due to the absence of metadata, rendering the selection process intricate. This research seeks to establish synergies between these disparate domains by advancing innovative methodologies. The study delves into the unexplored domain of harnessing data augmentation, particularly leveraging transcompiler-based techniques, to enhance the discernment of code clones across different programming languages. Drawing upon the insights derived from transcompilers, this investigation applies data augmentation through source-to-source translation, exemplified by the Transcoder. This method extends the applicability of single-language models, such as the Graph Matching Network (GMN), to encompass cross-language detection. Concurrently, a program code classification model is introduced, leveraging Convolutional Neural Networks (CNNs) to discern algorithms based on structural features (SFs). These SFs are extracted from program codes and subsequently transformed into a one-hot binary matrix. The CNN model undergoes meticulous fine-tuning, optimizing its structural configurations and hyperparameters for superior algorithm classification. This research signifies an integration of disparate realms, intertwining cross-language clone detection and algorithm identification within a unified framework. The exploration of data augmentation for cross-language clone detection and the employment of CNN-GMNs for algorithm identification converge to furnish significant contributions to both domains. These insights, thus, hold promise for advancing the fields of software engineering and programming education

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Out of step: Code clone detection for mobile apps across different language codebases

Jimenez,

Rakić,

Takahashi

et al. 2024

Science of Computer Programming

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Detection and Classification of Cross-language Code Clone Types by Filtering the Nodes of ANTLR-generated Parse Tree

Cited by 5 publications

References 30 publications

CroLSSim: Cross‐language software similarity detector using hybrid approach of LSA‐based AST‐MDrep features and CNN‐LSTM model

CroLSSim: Cross‐language software similarity detector using hybrid approach of LSA‐based AST‐MDrep features and CNN‐LSTM model

Transcompiler CNN-GMN Based Cross Language Clone Detection and Algorithm Recognition

Out of step: Code clone detection for mobile apps across different language codebases

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