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Multiclass vulnerability and clone detection in Ethereum smart contracts using Block-wise abstract syntax tree based Federated Graph Neural Networks

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 123卷

作者： Sharma, Shruti Ratmele, Ankur Seth, Abhay Deep SVKMs NMIMS Dept Comp Engn STME Indore 453112 Madhya Pradesh India

Smart contracts on blockchain networks autonomously execute applications based on predefined conditions, making their security-critical due to the potential for significant financial losses from vulnerabilities. Current vulnerability detection algorithms commonly rely on expert- defined rules, which are prone to errors and insufficient for identifying complex vulnerability patterns. Given the immutability of smart contracts post-deployment, ensuring security before deployment is essential. This research presents Block-wise abstract syntax tree based Federated Graph Neural Networks (BAST-FeGNN), a novel approach combining block-wise abstract syntax tree and Federated Graph Neural Networks (FeGNN) to detect code clones and multiclass vulnerabilities in Ethereum smart contracts. The BAST-FeGNN method operates in three stages: it first extracts security-related patterns from the base code using an abstract syntax tree;then, it constructs and normalizes a contract graph using FeGNN to capture critical nodes, analyze data and control flows. This integration of graph-based feature extraction with pattern matching allows precise detection of vulnerabilities like access control issues, reentrancy, and unchecked calls, as well as identifying code clones. Finally, the method pools these features for comprehensive vulnerability detection. BAST-FeGNN significantly enhances vulnerability detection accuracy and scalability, outperforming existing models with an accuracy of 95.35%, recall of 95.58%, F1-score of 95.80%, and precision of 96.10%, making it a robust solution for securing blockchain applications.

关键词： abstract syntax tree Ethereum smart contracts Federated graph neural network Vulnerability and clone detection

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ATOM: Commit Message Generation Based on abstract syntax tree and Hybrid Ranking

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2022年第5期48卷 1800-1817页

作者： Liu, Shangqing Gao, Cuiyun Chen, Sen Nie, Lun Yiu Liu, Yang Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Harbin Inst Technol Sch Comp Sci & Technol Shenzhen 518055 Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

Commit messages record code changes (e.g., feature modifications and bug repairs) in natural language, and are useful for program comprehension. Due to the frequent updates of software and time cost, developers are generally unmotivated to write commit messages for code changes. Therefore, automating the message writing process is necessitated. Previous studies on commit message generation have been benefited from generation models or retrieval models, but the code structure of changed code, i.e., AST, which can be important for capturing code semantics, has not been explicitly involved. Moreover, although generation models have the advantages of synthesizing commit messages for new code changes, they are not easy to bridge the semantic gap between code and natural languages which could be mitigated by retrieval models. In this paper, we propose a novel commit message generation model, named ATOM, which explicitly incorporates the abstract syntax tree for representing code changes and integrates both retrieved and generated messages through hybrid ranking. Specifically, the hybrid ranking module can prioritize the most accurate message from both retrieved and generated messages regarding one code change. We evaluate the proposed model ATOM on our dataset crawled from 56 popular Java repositories. Experimental results demonstrate that ATOM increases the state-of-the-art models by 30.72 percent in terms of BLEU-4 (an accuracy measure that is widely used to evaluate text generation systems). Qualitative analysis also demonstrates the effectiveness of ATOM in generating accurate code commit messages.

关键词： Syntactics Semantics Atomic measurements Hybrid power systems Benchmark testing Writing Java Commit message generation code changes abstract syntax tree

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Deep Learning With Customized abstract syntax tree for Bug Localization

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IEEE ACCESS 2019年 7卷 116309-116320页

作者： Liang, Hongliang Sun, Lu Wang, Meilin Yang, Yuxing Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China China Informat Technol Secur Evaluat Ctr Beijing 100085 Peoples R China

Given a bug report, bug localization technique can help developers automatically locate potential buggy files. Information retrieval and deep learning approaches have been applied in bug localization by extracting lexical features in bug reports and syntactic features in source code files, though they fail to utilize the structural and semantic information of source code files. In this paper, we present a bug localization system CAST, which exploits deep learning and customized abstract syntax trees of programs to locate potential buggy source files automatically and effectively. Specifically, CAST extracts both lexical semantics in bug reports (e.g., words) and source files (e.g., method names) and program semantics in source files (e.g., abstract syntax tree, AST). Moreover, CAST enhances the tree-based convolutional neural network (TBCNN) model with customized ASTs, which distinguish between user-defined methods and system-provided ones to reflect their contributions leading to defects. Furthermore, customized ASTs group the syntactic entities with similar semantics and prune the ones with little or redundant semantics in order to facilitate the learning performance. Experimental results on four widely-used software projects show that CAST significantly outperforms the state-of-the-art methods in locating the buggy source files.

关键词： abstract syntax tree bug localization convolutional neural network deep learning method invocation

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abstract syntax tree Analysis for Plagiarism Detection

Abstract Syntax Tree Analysis for Plagiarism Detection

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作者： Erik A. Nilsson Linkoping University

学位级别：硕士

Today, universities rely heavily on systems for detecting plagiarism in students' essays and reports. Code submissions however require specific tools. A number of approaches to finding plagiarisms in code have already been tried, including techniques based on comparing textual transformations of code, token strings, parse trees and graph representations. In this master's thesis, a new system, cojac, is presented which combines textual, tree and graph techniques to detect a broad spectrum of plagiarism attempts. The system finds plagiarisms in C, C++ and Ada source files. This thesis discusses the method used for obtaining parse trees from the source code and the abstract syntax tree analysis. For comparison of syntax trees, we generate sets of fingerprints, digest forms of trees, which makes the comparison algorithm more scalable. To evaluate the method, a set of benchmark files have been constructed containing plagiarism scenarios which was analyzed both by our system and Moss, another available system for plagiarism detection in code. The results show that our abstract syntax tree analysis can effectively detect plagiarisms such as changing the format of the code and renaming of identifiers and is at least as effective as Moss for detecting plagiarisms of these kinds.

关键词： abstract syntax tree plagiarism clone detection fingerprint

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A Heterogeneous Graph to abstract syntax tree Framework for Text-to-SQL

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第11期45卷 13796-13813页

作者： Cao, Ruisheng Chen, Lu Li, Jieyu Zhang, Hanchong Xu, Hongshen Zhang, Wangyou Yu, Kai Shanghai Jiao Tong Univ X LANCE Lab MoE Key Lab Artificial Intelligence Dept Comp Sci & EngnAI Inst Shanghai 200240 Peoples R China

Text-to-SQL is the task of converting a natural language utterance plus the corresponding database schema into a SQL program. The inputs naturally form a heterogeneous graph while the output SQL can be transduced into an abstract syntax tree (AST). Traditional encoder-decoder models ignore higher-order semantics in heterogeneous graph encoding and introduce permutation biases during AST construction, thus incapable of exploiting the refined structure knowledge precisely. In this work, we propose a generic heterogeneous graph to abstract syntax tree (HG2AST) framework to integrate dedicated structure knowledge into statistics-based models. On the encoder side, we leverage a line graph enhanced encoder (LGESQL) to iteratively update both node and edge features through dual graph message passing and aggregation. On the decoder side, a grammar-based decoder first constructs the equivalent SQL AST and then transforms it into the desired SQL via post-processing. To avoid over-fitting permutation biases, we propose a golden tree-oriented learning (GTL) algorithm to adaptively control the expanding order of AST nodes. The graph encoder and tree decoder are combined into a unified framework through two auxiliary modules. Extensive experiments on various text-to-SQL datasets, including single/multi-table, single/cross-domain, and multilingual settings, demonstrate the superiority and broad applicability.

关键词： Structured Query Language Decoding Databases Syntactics Semantics Task analysis Computational modeling abstract syntax tree grammar-based constrained decoding heterogeneous graph neural network knowledge-driven natural language processing permutation invariant problem text -to-SQL

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A Novel Neural Source Code Representation Based on abstract syntax tree 19

A Novel Neural Source Code Representation Based on Abstract ...

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41st IEEE/ACM International Conference on Software Engineering (ICSE)

作者： Zhang, Jian Wang, Xu Zhang, Hongyu Sun, Hailong Wang, Kaixuan Liu, Xudong Beihang Univ SKLSDE Lab Sch Comp Sci & Engn Beijing Peoples R China Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing Peoples R China Univ Newcastle Callaghan NSW Australia

ISBN: (纸本)9781728108698

Exploiting machine learning techniques for analyzing programs has attracted much attention. One key problem is how to represent code fragments well for follow-up analysis. Traditional information retrieval based methods often treat programs as natural language texts, which could miss important semantic information of source code. Recently, state-of-the-art studies demonstrate that abstract syntax tree (AST) based neural models can better represent source code. However, the sizes of ASTs are usually large and the existing models are prone to the long-term dependency problem. In this paper, we propose a novel AST-based Neural Network (ASTNN) for source code representation. Unlike existing models that work on entire ASTs, ASTNN splits each large AST into a sequence of small statement trees, and encodes the statement trees to vectors by capturing the lexical and syntactical knowledge of statements. Based on the sequence of statement vectors, a bidirectional RNN model is used to leverage the naturalness of statements and finally produce the vector representation of a code fragment. We have applied our neural network based source code representation method to two common program comprehension tasks: source code classification and code clone detection. Experimental results on the two tasks indicate that our model is superior to state-of-the-art approaches.

关键词： abstract syntax tree source code representation neural network code classification code clone detection

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Loop Transformations using Clang's abstract syntax tree 21

Loop Transformations using Clang's Abstract Syntax Tree

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50th International Conference on Parallel Processing (ICPP)

作者： Kruse, Michael Argonne Natl Lab Lemont IL 60439 USA

ISBN: (纸本)9781450384414

OpenMP 5.1 introduced the first loop nest transformation directives unroll and tile, and more are expected to be included in OpenMP 6.0. We discuss the two abstract syntax tree (AST) representations used by Clang's implementation that is currently under development. The first representation is designed for compatibility with the existing implementation and stores the transformed loop nest in a shadow AST next to the syntactical AST. The second representation introduces a new meta AST-node OMPCa nonica'Loop that guarantees that the semantic requirements of an OpenMP loop are met, and a CanonicalLoopInfo type that the OpenMPIRBuilder uses to represent literal and transformed loops. This second approach provides a better abstraction of loop semantics, removes the need for shadow AST nodes that are only relevant for code generation, allows sharing the implementation with other front-ends such as flang, but depends on the OpenMPIRBuilder which is currently under development.

关键词： OpenMP Clang abstract syntax tree semantic analysis code generation

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Malicious Code Utilization Chain Detection Scheme based on abstract syntax tree 6

Malicious Code Utilization Chain Detection Scheme based on A...

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6th IEEE Advanced Information Technology, Electronic and Automation Control Conference (IEEE IAEAC)

作者： Si, Guanlin Zhang, Yue Li, Min Jing, Sen North China Elect Power Res Inst Co Ltd Beijing 100045 Peoples R China

ISBN: (数字)9781665458641

ISBN: (纸本)9781665458641

The vulnerability based on deserialization is a code defect that does great harm but is not easy to be detected by the machine. This vulnerability requires a combination of multiple trigger functions. Once a complete utilization chain is formed, arbitrary file reading and even remote command execution can be achieved. In this scheme, the malicious code generates an abstract syntax tree through syntax analysis, takes the class name and member method name as the root node and intermediate node, uses keyword regular matching as the basis for identifying the main functions of member methods, and takes the main functions of member methods as leaf nodes. Finally, the code audit problem is transformed into the path traversal problem of the spanning tree. Through the spanning tree traversal algorithm, the automatic detection of malicious code is realized, and the efficiency and accuracy of code audit are improved.

关键词： malicious code utilization chain detection abstract syntax tree F1-score

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Reverse Engineering of Source Code to Sequence Diagram Using abstract syntax tree 3

Reverse Engineering of Source Code to Sequence Diagram Using...

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3rd International Conference on Data and Software Engineering (ICoDSE)

作者： Fauzil, Esa Hendradjaya, Bayu Sunindyo, Wikan Danar Bandung Technol Inst Sch Elect Engn & Informat Bandung Indonesia

ISBN: (纸本)9781509056712

Reverse engineering from a source code to a sequence diagram can be very important in the software maintenance process. On the process of reverse engineering, there are a lot of models/approaches that can be used. Extract abstract -present model is one model that can be applied because every stage in the model can represent reverse engineering process. On stages in the model, the extracted source code is converted into a specific structure for easy analyzed in the next process. AST (abstract syntax tree) is one of the structures that can be used in the extraction process. AST is commonly used as a data structure in the compiler. This research focus on the process of reverse engineering of source code to sequence diagrams. AST is used to generate the sequence diagram. We have implemented this idea in an application called REVUML. REVUML has helped in the process of understanding the flow of the source code in a software application. Our research concludes that the structure of AST has been able to assist in the reverse engineering process. Additionally our implementation has shown that AST can reveal sequences of statements in the source code which can help developers in the reverse engineering process.

关键词： reverse engineering source code abstract syntax tree sequence diagram

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Source Code Plagiarism Detection Based on abstract syntax tree Fingerprintings 17

Source Code Plagiarism Detection Based on Abstract Syntax Tr...

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17th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) / 3rd International Conference on Artificial Intelligence and Internet of Things (AIoT)

作者： Suttichaya, Vasin Eakvorachai, Niracha Lurkraisit, Tunchanok Mahidol Univ Fac Engn Dept Comp Engn 25-25 Salaya Phuttamonthon 73170 Nakhon Pathom Thailand Mahidol Univ Int Coll Dept Comp Engn 999 Salaya Phuttamonthon 73170 Nakhon Pathom Thailand

ISBN: (数字)9781665457279

ISBN: (纸本)9781665457279

.abstract syntax tree (AST) is an abstract logical structure of source code represented as a tree. This research utilizes information of fingerprinting with AST to locate the similarities between source codes. The proposed method can detect plagiarism in source codes using the number of duplicated logical structures. The structural information of program is stored in the fingerprints format. Then, the fingerprints of source codes are compared to identify number of similar nodes. The final output is calculated from number of similar nodes known as similarities scores. The result shows that the proposed method accurately captures the common modification techniques from basic to advance.

关键词： Source Code Similarities Plagiarism Detection MOSS abstract syntax tree Fingerprinting

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