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Academic literature on the topic 'Abstract syntax tree'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 June 2025

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Journal articles on the topic "Abstract syntax tree"

1

Fu, Deqiang, Yanyan Xu, Haoran Yu, and Boyang Yang. "WASTK: A Weighted Abstract Syntax Tree Kernel Method for Source Code Plagiarism Detection." Scientific Programming 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/7809047.

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In this paper, we introduce a source code plagiarism detection method, named WASTK (Weighted Abstract Syntax Tree Kernel), for computer science education. Different from other plagiarism detection methods, WASTK takes some aspects other than the similarity between programs into account. WASTK firstly transfers the source code of a program to an abstract syntax tree and then gets the similarity by calculating the tree kernel of two abstract syntax trees. To avoid misjudgment caused by trivial code snippets or frameworks given by instructors, an idea similar to TF-IDF (Term Frequency-Inverse Document Frequency) in the field of information retrieval is applied. Each node in an abstract syntax tree is assigned a weight by TF-IDF. WASTK is evaluated on different datasets and, as a result, performs much better than other popular methods like Sim and JPlag.

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2

Ding, Jiaman, Weikang Fu, and Lianyin Jia. "Deep Forest and Pruned Syntax Tree-Based Classification Method for Java Code Vulnerability." Mathematics 11, no. 2 (2023): 461. http://dx.doi.org/10.3390/math11020461.

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The rapid development of J2EE (Java 2 Platform Enterprise Edition) has brought unprecedented severe challenges to vulnerability mining. The current abstract syntax tree-based source code vulnerability classification method does not eliminate irrelevant nodes when processing the abstract syntax tree, resulting in a long training time and overfitting problems. Another problem is that different code structures will be translated to the same sequence of tree nodes when processing abstract syntax trees using depth-first traversal, so in this process, the depth-first algorithm will lead to the loss of semantic structure information which will reduce the accuracy of the model. Aiming at these two problems, we propose a deep forest and pruned syntax tree-based classification method (PSTDF) for Java code vulnerability. First, the breadth-first traversal of the abstract syntax tree obtains the sequence of statement trees, next, pruning statement trees removes irrelevant nodes, then we use a depth-first based encoder to obtain the vector, and finally, we use deep forest as the classifier to get classification results. Experiments on publicly accessible vulnerability datasets show that PSTDF can reduce the loss of semantic structure information and effectively remove the impact of redundant information.

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3

Shen, Victor R. L. "Novel Code Plagiarism Detection Based on Abstract Syntax Tree and Fuzzy Petri Nets." International Journal of Engineering Education 1, no. 1 (2019): 46–56. http://dx.doi.org/10.14710/ijee.1.1.46-56.

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Those students who major in computer science and/or engineering are required to design program codes in a variety of programming languages. However, many students submit their source codes they get from the Internet or friends with no or few modifications. Detecting the code plagiarisms done by students is very time-consuming and leads to the problems of unfair learning performance evaluation. This paper proposes a novel method to detect the source code plagiarisms by using a high-level fuzzy Petri net (HLFPN) based on abstract syntax tree (AST). First, the AST of each source code is generated after the lexical and syntactic analyses have been done. Second, token sequence is generated based on the AST. Using the AST can effectively detect the code plagiarism by changing the identifier or program statement order. Finally, the generated token sequences are compared with one another using an HLFPN to determine the code plagiarism. Furthermore, the experimental results have indicated that we can make better determination to detect the code plagiarism.

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4

Walker, Matt, Parssa Khazra, Anto Nanah Ji, Hongru Wang, and Franck van Breugel. "jpf-logic." ACM SIGSOFT Software Engineering Notes 48, no. 1 (2023): 32–36. http://dx.doi.org/10.1145/3573074.3573083.

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We present jpf-logic, an extension of the model checker Java PathFinder (JPF). Our extension jpf-logic provides a framework to check properties expressed in temporal logics such as linear temporal logic (LTL) and computation tree logic (CTL). To support a logic in our framework, we (1) implement a parser for the logic, (2) develop a hierarchy of classes that represent the abstract syntax of the logic and implement a transformation from parse trees of formulas to the corresponding abstract syntax trees, and (3) implement a model checking algorithm that takes as input an abstract syntax tree of a formula and a partial transition system. The latter represents a model of the Java application. All three components have been implemented for CTL. The first two have been implemented for LTL.

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5

Lee, Seung-Hyung, and Young-Jae Song. "Aspect Mining Process Design Using Abstract Syntax Tree." Journal of the Korea Contents Association 11, no. 5 (2011): 75–83. http://dx.doi.org/10.5392/jkca.2011.11.5.075.

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6

Kalibera, Tomas, Petr Maj, Floreal Morandat, and Jan Vitek. "A fast abstract syntax tree interpreter for R." ACM SIGPLAN Notices 49, no. 7 (2014): 89–102. http://dx.doi.org/10.1145/2674025.2576205.

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7

Zhang, Zhining, Liang Wan, Kun Chu, Shusheng Li, Haodong Wei, and Lu Tang. "JACLNet:Application of adaptive code length network in JavaScript malicious code detection." PLOS ONE 17, no. 12 (2022): e0277891. http://dx.doi.org/10.1371/journal.pone.0277891.

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Currently, JavaScript malicious code detection methods are becoming more and more effective. Still, the existing methods based on deep learning are poor at detecting too long or too short JavaScript code. Based on this, this paper proposes an adaptive code length deep learning network JACLNet, composed of convolutional block RDCNet, BiLSTM and Transfrom, to capture the association features of the variable distance between codes. Firstly, an abstract syntax tree recombination algorithm is designed to provide rich syntax information for feature extraction. Secondly, a deep residual convolution block network (RDCNet) is designed to capture short-distance association features between codes. Finally, this paper proposes a JACLNet network for JavaScript malicious code detection. To verify that the model presented in this paper can effectively detect variable JavaScript code, we divide the datasets used in this paper into long text dataset DB_Long; short text dataset DB_Short, original dataset DB_Or and enhanced dataset DB_Re. In DB_Long, our method’s F1 − score is 98.87%, higher than that of JSContana by 2.52%. In DB_Short, our method’s F1-score is 97.32%, higher than that of JSContana by 7.79%. To verify that the abstract syntax tree recombination algorithm proposed in this paper can provide rich syntax information for subsequent models, we conduct comparative experiments on DB_Or and DB_Re. In DPCNN+BiLSTM, F1-score with abstract syntax tree recombination increased by 1.72%, and in JSContana, F1-score with abstract syntax tree recombination increased by 1.50%. F1-score with abstract syntax tree recombination in JACNet improved by 1.00% otherwise unused.

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8

Khatoon, Tahira. "Abstract Syntax Tree Based Clone Detection for Java Projects." IOSR Journal of Engineering 02, no. 12 (2012): 45–47. http://dx.doi.org/10.9790/3021-021234547.

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9

Neamtiu, Iulian, Jeffrey S. Foster, and Michael Hicks. "Understanding source code evolution using abstract syntax tree matching." ACM SIGSOFT Software Engineering Notes 30, no. 4 (2005): 1–5. http://dx.doi.org/10.1145/1082983.1083143.

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10

Patra. "JAVASCRIPT INTERPRETER USING NON RECURSIVE ABSTRACT SYNTAX TREE BASED STACK." American Journal of Applied Sciences 10, no. 4 (2013): 403–13. http://dx.doi.org/10.3844/ajassp.2013.403.413.

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