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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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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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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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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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Long short-term memory on abstract syntax tree for SQL injection detection

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IET SOFTWARE 2021年第2期15卷 188-197页

作者： Zhuo, Z. Cai, T. Zhang, X. Lv, F. Huawei Technol Data Secur Dept Chengdu Peoples R China Southwestern Univ Finance & Econ Ctr Stat Res Chengdu Peoples R China Southwestern Univ Finance & Econ Sch Stat Chengdu Peoples R China

SQL injection attack (SQLIA) is a code injection technique, used to attack data-driven applications by executing malicious SQL statements. Techniques like pattern matching, software testing and grammar analysis etc. are frequently used to prevent such attack. However, major bottlenecks still remain in detecting SQLIA with bypassing techniques, getting access to source code and requiring an additional manual operation to extract features. The authors propose a novel detection approach based on long short-term memory and abstract syntax tree, which could detect SQLIAs from the raw query strings and work under SQL detection bypassing scenario. Our deep learning technique explicitly uses both context and syntax information that previous methods failed to fully grasp. Experimental results clearly illustrate the superior performance of our method compared to other existing works when detecting with complete SQL raw queries.

关键词： complete SQL raw queries long short-term memory pattern matching bypassing techniques abstract syntax tree Relational databases source code recurrent neural nets SQL detection bypassing scenario deep learning (artificial intelligence) feature extraction SQLIA grammar analysis query processing attack data-driven applications raw query strings malicious SQL statements SQL SQL injection attack syntax information deep learning technique Data handling techniques code injection technique SQL injection detection Information retrieval techniques software testing source code (software)

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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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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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Exploring the Effectiveness of abstract syntax tree Patterns for Algorithm Recognition 4

Exploring the Effectiveness of Abstract Syntax Tree Patterns...

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4th International Conference on Code Quality (ICCQ)

作者： Neumuller, Denis Straub, Raphael Sihler, Florian Tichy, Matthias Univ Ulm Ulm Germany

ISBN: (纸本)9798350366464;9798350366471

The automated recognition of algorithm implementations can support many software maintenance and reengineering activities by providing knowledge about the concerns present in the code base. Moreover, recognizing inefficient algorithms like Bubble Sort and suggesting superior alternatives from a library can help in assessing and improving the quality of a system. Approaches from related work suffer from usability as well as scalability issues and their accuracy is not evaluated. In this paper, we investigate how well our approach based on the abstract syntax tree of a program performs for automatic algorithm recognition. To this end, we have implemented a prototype consisting of: A domain-specific language designed to capture the key features of an algorithm and used to express a search pattern on the abstract syntax tree, a matching algorithm to find these features, and an initial catalog of "ready to use" patterns. To create our search patterns we performed a web search using the algorithm name and described key features of the found reference implementations with our domain-specific language. We evaluate our prototype on a subset of the BigCloneEval benchmark containing algorithms like Fibonacci, Bubble Sort, and Binary Search. We achieve an average F-1-score of 0.74 outperforming the large language model Codellama which attains 0.35. Additionally, we use multiple code clone detection tools as a baseline for comparison, achieving a recall of 0.62 while the best-performing tool reaches 0.20.

关键词： algorithm recognition program comprehension pattern matching abstract syntax tree domain-specific language reverse engineering maintenance

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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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Node Embedding of the abstract syntax tree for Source Code Representation 39th

Node Embedding of the Abstract Syntax Tree for Source Code R...

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39th CCF National Conference on Computer Applications

作者： Chen, Chang-Feng Zain, Azlan Mohd Zhou, Kai-Qing Jishou Univ Coll Comp Sci & Engn Jishou 416000 Peoples R China Univ Teknol Malaysia Fac Comp Skudai 80310 Johor Malaysia Jishou Univ Sch Commun & Elect Engn Jishou 416000 Peoples R China

ISBN: (纸本)9789819796700;9789819796717

Source code representation has garnered significant attention owing to its critical role in solving software engineering problems. There are various methodologies for representing code, and the abstract syntax tree (AST) is one of the most widely used techniques. However, current AST-based approaches need to pay more attention to the impact of AST generation, resulting in high duplication of the tree structure. Additionally, most AST-based methods struggle to balance homophily and structural equivalence in sequence sampling while failing to express semantic information in node feature learning. These limitations lead to poor source code representation and reduced performance in solving software engineering problems. To address these challenges, we propose a novel model that combines a deduplication algorithm, a hybrid sampling strategy, and an optimized skip-gram feature learning approach to represent source code. To evaluate the efficacy of our proposed model, we conduct experiments on OJ datasets for program classification. Our results demonstrate that the proposed model reduces the duplication of the tree structure and improves the representation performance of source code compared with other methods.

关键词： Source code representation abstract syntax tree skip-gram code classification

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