Journal articles: 'Abstract syntax tree'

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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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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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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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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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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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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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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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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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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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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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Söderberg, Emma, Torbjörn Ekman, Görel Hedin, and Eva Magnusson. "Extensible intraprocedural flow analysis at the abstract syntax tree level." Science of Computer Programming 78, no. 10 (2013): 1809–27. http://dx.doi.org/10.1016/j.scico.2012.02.002.

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Liang, Hongliang, Lu Sun, Meilin Wang, and Yuxing Yang. "Deep Learning With Customized Abstract Syntax Tree for Bug Localization." IEEE Access 7 (2019): 116309–20. http://dx.doi.org/10.1109/access.2019.2936948.

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Bui, Nghi D. Q., Yijun Yu, and Lingxiao Jiang. "TreeCaps: Tree-Based Capsule Networks for Source Code Processing." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (2021): 30–38. http://dx.doi.org/10.1609/aaai.v35i1.16074.

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Recently program learning techniques have been proposed to process source code based on syntactical structures (e.g., abstract syntax trees) and/or semantic information (e.g., dependency graphs). While graphs may be better than trees at capturing code semantics, constructing the graphs from code inputs through the semantic analysis of multiple viewpoints can lead to inaccurate noises for a specific software engineering task. Compared to graphs, syntax trees are more precisely defined on the grammar and easier to parse; unfortunately, previous tree-based learning techniques have not been able to learn semantic information from trees to achieve better accuracy than graph-based techniques. We have proposed a new learning technique, named TreeCaps, by fusing together capsule networks with tree-based convolutional neural networks to achieve a learning accuracy higher than some existing graph-based techniques while it is based only on trees. TreeCaps introduces novel variable-to-static routing algorithms into the capsule networks to compensate for the loss of previous routing algorithms. Aside from accuracy, we also find that TreeCaps is the most robust to withstand those semantic-preserving program transformations that change code syntax without modifying the semantics. Evaluated on a large number of Java and C/C++ programs, TreeCaps models outperform prior deep learning models of program source code, in terms of both accuracy and robustness for program comprehension tasks such as code functionality classification and function name prediction. Our implementation is publicly available at: https://github.com/bdqnghi/treecaps.

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Roberts, Mark D. "Ultrametric Distance in Syntax." Prague Bulletin of Mathematical Linguistics 103, no. 1 (2015): 111–30. http://dx.doi.org/10.1515/pralin-2015-0006.

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Abstract Phrase structure trees have a hierarchical structure. In many subjects, most notably in taxonomy such tree structures have been studied using ultrametrics. Here syntactical hierarchical phrase trees are subject to a similar analysis, which is much simpler as the branching structure is more readily discernible and switched. The ambiguity of which branching height to choose, is resolved by postulating that branching occurs at the lowest height available. An ultrametric produces a measure of the complexity of sentences: presumably the complexity of sentences increases as a language is acquired so that this can be tested. All ultrametric triangles are equilateral or isosceles. Here it is shown that X̅ structure implies that there are no equilateral triangles. Restricting attention to simple syntax a minimum ultrametric distance between lexical categories is calculated. A matrix constructed from this ultrametric distance is shown to be different than the matrix obtained from features. It is shown that the definition of C-COMMAND can be replaced by an equivalent ultrametric definition. The new definition invokes a minimum distance between nodes and this is more aesthetically satisfying than previous varieties of definitions. From the new definition of C-COMMAND follows a new definition of of the central notion in syntax namely GOVERNMENT.

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Kim, Young-Chul, and Jaeyoung Choi. "A Program Similarity Evaluation using Keyword Extraction on Abstract Syntax Tree." KIPS Transactions:PartA 12A, no. 2 (2005): 109–16. http://dx.doi.org/10.3745/kipsta.2005.12a.2.109.

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Cai, Ziyi, Lu Lu, and Shaojian Qiu. "An Abstract Syntax Tree Encoding Method for Cross-Project Defect Prediction." IEEE Access 7 (2019): 170844–53. http://dx.doi.org/10.1109/access.2019.2953696.

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Zhuo, Z., T. Cai, X. Zhang, and F. Lv. "Long short‐term memory on abstract syntax tree for SQL injection detection." IET Software 15, no. 2 (2021): 188–97. http://dx.doi.org/10.1049/sfw2.12018.

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Duracik, Michal, Patrik Hrkut, Emil Krsak, and Stefan Toth. "Abstract Syntax Tree Based Source Code Antiplagiarism System for Large Projects Set." IEEE Access 8 (2020): 175347–59. http://dx.doi.org/10.1109/access.2020.3026422.

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Son, Hyun Seung, and R. Young Chul Kim. "A Visualization of Secure Vulnerability in Code using Abstract Syntax Tree Metamodel." Journal of Security Engineering 14, no. 1 (2017): 21–32. http://dx.doi.org/10.14257/jse.2017.02.04.

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Kim, Jaehyun, and Yangsun Lee. "A Study on Abstract Syntax Tree for Development of a JavaScript Compiler." International Journal of Grid and Distributed Computing 11, no. 6 (2018): 37–48. http://dx.doi.org/10.14257/ijgdc.2018.11.6.04.

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Pogodalla, Sylvain. "A syntax-semantics interface for Tree-Adjoining Grammars through Abstract Categorial Grammars." Journal of Language Modelling 5, no. 3 (2018): 527. http://dx.doi.org/10.15398/jlm.v5i3.193.

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Yao, Wenjun, Ying Jiang, and Yang Yang. "The Metric for Automatic Code Generation Based on Dynamic Abstract Syntax Tree." International Journal of Digital Crime and Forensics 15, no. 1 (2023): 1–20. http://dx.doi.org/10.4018/ijdcf.325062.

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In order to improve the efficiency and quality of software development, automatic code generation technology is the current focus. The quality of the code generated by the automatic code generation technology is also an important issue. However, existing metrics for code automatic generation ignore that the programming process is a continuous dynamic changeable process. So the metric is a dynamic process. This article proposes a metric method based on dynamic abstract syntax tree (DAST). More specifically, the method first builds a DAST through the interaction in behavior information between the automatic code generation tool and programmer. Then the measurement contents are extracted on the DAST. Finally, the metric is completed with contents extracted. The experiment results show that the method can effectively realize the metrics of automatic code generation. Compared with the MAST method, the method in this article can improve the convergence speed by 80% when training the model, and can shorten the time-consuming by an average of 46% when doing the metric prediction.

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Wang, Yanlin, and Hui Li. "Code Completion by Modeling Flattened Abstract Syntax Trees as Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (2021): 14015–23. http://dx.doi.org/10.1609/aaai.v35i16.17650.

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Code completion has become an essential component of integrated development environments. Contemporary code completion methods rely on the abstract syntax tree (AST) to generate syntactically correct code. However, they cannot fully capture the sequential and repetitive patterns of writing code and the structural information of the AST. To alleviate these problems, we propose a new code completion approach named CCAG, which models the flattened sequence of a partial AST as an AST graph. CCAG uses our proposed AST Graph Attention Block to capture different dependencies in the AST graph for representation learning in code completion. The sub-tasks of code completion are optimized via multi-task learning in CCAG, and the task balance is automatically achieved using uncertainty without the need to tune task weights. The experimental results show that CCAG has superior performance than state-of-the-art approaches and it is able to provide intelligent code completion.

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He, Xinglu, Pengfei Wang, Kai Lu, and Xu Zhou. "EtWExplorer: Multi-Priority Scheduling Path Exploration Technology Based on Abstract Syntax Tree Analysis." Applied Sciences 12, no. 19 (2022): 10182. http://dx.doi.org/10.3390/app121910182.

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Symbolic execution is well known as a dynamic vulnerability discovery technique. Its greatest advantage is the capability to analyze the execution information of the program and to explore the path in the program deterministically. This is a more accurate way to determine if there are vulnerabilities in a program than randomized testing by fuzzing. In addition, symbolic execution does not suffer from the problem of decreasing the capability to discover new paths as more paths are discovered, similar to that caused by random-based fuzzing. However, the reason why symbolic execution is not widely used in vulnerability discovery is mainly due to the state space explosion in the program. The state space explosion severely affects the applicability of symbolic execution. To further improve the applicability of symbolic execution, this paper proposes a path exploration technology based on abstract syntax tree analysis. With the distance between the expression generated by the symbolic execution of the repeat location and the “unsatisfiable” condition of the “unsat” state, we can perform multi-priority scheduling for the repeat location state, thus mitigating the impact of the state space explosion on path exploration. We proposed and implemented EtWExplorer, a multi-priority scheduling technique based on abstract syntax tree analysis. With this technique, we can significantly improve the capability of symbolic execution to discover unknown paths even in state space exploration. Experiments show that EtWExplorer introduces a performance overhead of 72% in the worst case and can improve performance by 294% in the best case. EtWExplorer has a 95% improvement in state space explosion mitigation capability and a 199% to 983% improvement in the path exploration capability of block coverage and a 181% to 1047% improvement in the path exploration capability of edge coverage when facing programs that cause a state space explosion.

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Kaur, Amandeep, and Munish Saini. "Enhancing the Software Clone Detection in BigCloneBench." International Journal of Open Source Software and Processes 12, no. 3 (2021): 17–31. http://dx.doi.org/10.4018/ijossp.2021070102.

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In the software system, the code snippets that are copied and pasted in the same software or another software result in cloning. The basic cause of cloning is either a programmer‘s constraint or language constraints. An increase in the maintenance cost of software is the major drawback of code clones. So, clone detection techniques are required to remove or refactor the code clone. Recent studies exhibit the abstract syntax tree (AST) captures the structural information of source code appropriately. Many researchers used tree-based convolution for identifying the clone, but this technique has certain drawbacks. Therefore, in this paper, the authors propose an approach that finds the semantic clone through square-based convolution by taking abstract syntax representation of source code. Experimental results show the effectiveness of the approach to the popular BigCloneBench benchmark.

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Bhat, Spurthi, Rutuja Bhirud, and Vaishnavi Bhokare. "Survey on Various Syntax Analyzer Tools." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (2022): 109–15. http://dx.doi.org/10.22214/ijraset.2022.46588.

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Abstract: Syntax analysis forms the second phase of a compiler. Syntax Analyzer basically takes the input of tokens from the lexical analyzer and parses the source code according to the production rules to detect errors in the code. The syntax analyzer gives an output in the form of a parse tree. The syntax analysis techniques can be classified as top-down parsing and bottom-up parsing. These categories can be further subdivided into recursive descent, LL (1), operator precedence, LR (0), SLR (1), CLR (1) and LALR (1) respectively. Various parser generators can generate parsers of these types which have been studied and analyzed in this paper such as Beaver, Tatoo, APG etc. This paper provides an overview of all these tools with respective to their working, advantages and features. These syntax analyzer tools can be used for different purposes according to the user.

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Ko, Jong Won. "Design of MDA Based Model Transformation Profile for Model Verification." Applied Mechanics and Materials 224 (November 2012): 69–72. http://dx.doi.org/10.4028/www.scientific.net/amm.224.69.

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Validation of existing software design models and transformed target models for the study, mainly checking (Model Checking) with a code-based software designed to define in the abstract syntax tree or on the models generated using refactoring on design models for refinery operations and define how to perform. The problem with these traditional research methods, but the first model, design model for checking the information with the model by defining a formal representation in the form of an abstract syntax tree, as you've shown how to perform validation of UML design model. Additional steps need to define more complex due to a software problem that is not the way to the model suitable for model transformation verification. In this paper, as defined in the MDA based model transformation studies of a graph based mode transformation, and how to perform model transformation verification through improving graph comparison algorithm and model property information.

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Kikuchi, Hiroshi, Takaaki Goto, Mitsuo Wakatsuki, and Tetsuro Nishino. "A Source Code Plagiarism Detecting Method Using Sequence Alignment with Abstract Syntax Tree Elements." International Journal of Software Innovation 3, no. 3 (2015): 41–56. http://dx.doi.org/10.4018/ijsi.2015070104.

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Learning to program is an important subject in computer science courses. During programming exercises, plagiarism by copying and pasting can lead to problems for fair evaluation. Some methods of plagiarism detection are currently available, such as sim. However, because sim is easily influenced by changing the identifier or program statement order, it fails to do enough to support plagiarism detection. In this paper, the authors propose a plagiarism detection method which is not influenced by changing the identifier or program statement order. The authors also explain our method's capabilities by comparing it to the sim plagiarism detector. Furthermore, the authors reveal how our method successfully detects the presence of plagiarism.

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Yu, Hang, Huiquan Gong, and Yawen Wang. "Design and implementation of fault injection based on abstract syntax tree of C Program." IOP Conference Series: Materials Science and Engineering 715 (January 3, 2020): 012034. http://dx.doi.org/10.1088/1757-899x/715/1/012034.

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Ullah, Farhan, Sohail Jabbar, and Fadi Al-Turjman. "Programmers' de-anonymization using a hybrid approach of abstract syntax tree and deep learning." Technological Forecasting and Social Change 159 (October 2020): 120186. http://dx.doi.org/10.1016/j.techfore.2020.120186.

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Singh, Pavitdeep, Satwinder Singh, and Jatinder Kaur. "Tool for generating code metrics for C# source code using abstract syntax tree technique." ACM SIGSOFT Software Engineering Notes 38, no. 5 (2013): 1–6. http://dx.doi.org/10.1145/2507288.2507312.

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Wang, Tiantian, Djoko Budi Santoso, Kechao Wang, and Xiaohong Su. "Automatic Grading for Complex Multifile Programs." Complexity 2020 (June 13, 2020): 1–15. http://dx.doi.org/10.1155/2020/3279053.

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This paper presents an automatic grading method DGRADER, which handles complex multifile programs. Both the dynamic and the static grading support multifile program analysis. So, it can be an advantage to handle complex programming problem which requires more than one program file. Dynamic analysis takes advantage of object file linker in compilation to link complex multifile program. The static grading module consists of the following steps. Firstly, the program is parsed into abstract syntax tree, which is mapped into abstract syntax tree data map. Then, the information of preprocessor is used for linking external sources called in main program by complex multifile program linker-fusion algorithm. Next, standardization process is performed for problematic code removal, unused function removal, and function sequence ordering based on function call. Finally, program matching successfully tackles structure variance problem by previous standardization process and by simple tree matching using tag classifier. The novelty of the approach is that it handles complex multifile program analysis with flexible grading with consideration of modularity and big scale of programming problem complexity. The results have shown improvement in grading precision which gives reliable grading score delivered with intuitive system.

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Li, Zhiming, Qing Wu, and Kun Qian. "Adabot: Fault-Tolerant Java Decompiler (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (2020): 13861–62. http://dx.doi.org/10.1609/aaai.v34i10.7203.

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Reverse Engineering has been an extremely important field in software engineering, it helps us to better understand and analyze the internal architecture and interrealtions of executables. Classical Java reverse engineering task includes disassembly and decompilation. Traditional Abstract Syntax Tree (AST) based disassemblers and decompilers are strictly rule defined and thus highly fault intolerant when bytecode obfuscation were introduced for safety concern. In this work, we view decompilation as a statistical machine translation task and propose a decompilation framework which is fully based on self-attention mechanism. Through better adaption to the linguistic uniqueness of bytecode, our model fully outperforms rule-based models and previous works based on recurrence mechanism.

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Ko, Jong Won, and Young Jae Song. "A Study on Model Transformation Mechanism Using Graph Comparison Algorithm, Abstract Factory Pattern and Bridge Pattern." Applied Mechanics and Materials 121-126 (October 2011): 2476–81. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.2476.

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In order to easily port mobile applications suitable for each platform, that have been developed under diverse development environment for individual wireless communication service providers, or redevelop them on a specific platform, it is required to reuse them at software model level that is a software development paradigm for MDA (Model Driven Architecture). Verification of existing design models and test models for the study, mainly checking (Model Checking) with a code-based software designed to define in the abstract syntax tree or on the models generated using refactoring on design models for refinery operations and define how to perform. The problem with these traditional research methods, but the first model, design model for checking the information with the model by defining a formal representation in the form of an abstract syntax tree, as you've shown how to perform verification of the model to perform refactoring. Additional steps need to define more complex due to a software problem that is not the way to the model suitable for optimization refactoring. In this paper, as defined in the MDA-based model transformation studies of a reusable model transformation patterns, abstract factory pattern and the bridge pattern given the pattern information automatically through the software model offers a way to perform refactoring..

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Xu, Shuangbin, Zehan Dai, Pingfan Guo, et al. "ggtreeExtra: Compact Visualization of Richly Annotated Phylogenetic Data." Molecular Biology and Evolution 38, no. 9 (2021): 4039–42. http://dx.doi.org/10.1093/molbev/msab166.

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Abstract We present the ggtreeExtra package for visualizing heterogeneous data with a phylogenetic tree in a circular or rectangular layout (https://www.bioconductor.org/packages/ggtreeExtra). The package supports more data types and visualization methods than other tools. It supports using the grammar of graphics syntax to present data on a tree with richly annotated layers and allows evolutionary statistics inferred by commonly used software to be integrated and visualized with external data. GgtreeExtra is a universal tool for tree data visualization. It extends the applications of the phylogenetic tree in different disciplines by making more domain-specific data to be available to visualize and interpret in the evolutionary context.

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Liu, Zhao, Yang Tian, and Haihua Yan. "An Approach of Conformance Verification between Design Models and Code Based on Abstract Syntax Tree." MATEC Web of Conferences 54 (2016): 07001. http://dx.doi.org/10.1051/matecconf/20165407001.

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Jinpa, Tenzin, and Yong Gao. "Code Representation Learning Using Prüfer Sequences (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 12977–78. http://dx.doi.org/10.1609/aaai.v36i11.21625.

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An effective and efficient encoding of the source code of a computer program is critical to the success of sequence-to-sequence deep neural network models for code representation learning. In this study, we propose to use the Prufer sequence of the Abstract Syntax Tree (AST) of a computer program to design a sequential representation scheme that preserves the structural information in an AST. Our representation makes it possible to develop deep-learning models in which signals carried by lexical tokens in the training examples can be exploited automatically and selectively based on their syntactic role and importance. Unlike other recently-proposed approaches, our representation is concise and lossless in terms of the structural information of the AST. Results from our experiment show that prufer-sequence-based representation is indeed highly effective and efficient.

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Darchuk, Nataliia. "AGAT-Syntax as component of computer-based Ukrainian grammar." Ukrainska mova, no. 4 (2020): 82–102. http://dx.doi.org/10.15407/ukrmova2020.04.089.

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Abstract: The article describes functional features of the syntactic module of computer-based Ukrainian grammar AGAT. This is a linguistic type of computer-aided syntactic analysis, which provides full information about syntactic units and categories, in particular, predicativity, coordinate and subordinate clauses, the categories of subject and predicate etc. The developed linguistic software provides syntactic analysis of a whole sentence in the form of a dependency tree and indicates the types of syntactic relations and links. The AGAT-syntax task is to identify all varieties of compatibility – predicative, subordinate, and coordinate – of each word in the text. The grammatical characteristics of the phrase directly depend on which part of the language its keyword belongs to. The lexical and grammatical nature of the word determines its compatibility to the other words. Accordingly, phrases can be divided into substantive, adjective, pronouns, numeral, verbal and adverbial. Computer sub-grammars of valencies of the said parts of the language are built by us on a single principle: a lexema is indicated, preposition that participates in government and a case of a substantive word form in the shape of a two-letter code. In theory, according to their composition words combinations (phrases) are divided into simple, complex and combined. Dependency tree is built from two elements – nodes and connections. Nodes are wordforms and connections are relationships between the main element (“master”) and dependent element (“slave”). It enables to describe a configuration, a form, external parameters of a sentence but this is not sufficient to describe a sentence structure. Thus, the syntactic analysis has two levels: the first one attributes to each binary pair a type of syntactic relationships on the level of morphological way of expression of a “master”; the second level attributes to the connection a type of syntactic relationships, which include: subjective, objective, attributive, adverbial, completive and appositive modifying.. In such a way, the cycle of automated syntactic analysis of Ukrainian texts is completed by determining the syntactic word-combination, identifying a type of syntactic link and a type of relationship. It provides full range of characteristics that can be used for systemic study of semantic and syntactic problems. Keywords: automated syntactic analysis, dependency tree, syntactic relations, syntactic links.

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Koparkar, Chaitanya, Mike Rainey, Michael Vollmer, Milind Kulkarni, and Ryan R. Newton. "Efficient tree-traversals: reconciling parallelism and dense data representations." Proceedings of the ACM on Programming Languages 5, ICFP (2021): 1–29. http://dx.doi.org/10.1145/3473596.

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Recent work showed that compiling functional programs to use dense, serialized memory representations for recursive algebraic datatypes can yield significant constant-factor speedups for sequential programs. But serializing data in a maximally dense format consequently serializes the processing of that data, yielding a tension between density and parallelism. This paper shows that a disciplined, practical compromise is possible. We present Parallel Gibbon, a compiler that obtains the benefits of dense data formats and parallelism. We formalize the semantics of the parallel location calculus underpinning this novel implementation strategy, and show that it is type-safe. Parallel Gibbon exceeds the parallel performance of existing compilers for purely functional programs that use recursive algebraic datatypes, including, notably, abstract-syntax-tree traversals as in compilers.

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Meng, Yao. "An Intelligent Code Search Approach Using Hybrid Encoders." Wireless Communications and Mobile Computing 2021 (July 1, 2021): 1–16. http://dx.doi.org/10.1155/2021/9990988.

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The intelligent code search with natural language queries has become an important researching area in software engineering. In this paper, we propose a novel deep learning framework At-CodeSM for source code search. The powerful code encoder in At-CodeSM, which is implemented with an abstract syntax tree parsing algorithm (Tree-LSTM) and token-level encoders, maintains both the lexical and structural features of source code in the process of code vectorizing. Both the representative and discriminative models are implemented with deep neural networks. Our experiments on the CodeSearchNet dataset show that At-CodeSM yields better performance in the task of intelligent code searching than previous approaches.

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Ordóñez Camacho, Diego. "Traducción automatizada de programas entre lenguajes de operaciones." Enfoque UTE 3, no. 1 (2012): 29–42. http://dx.doi.org/10.29019/enfoqueute.v3n1.8.

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Los lenguajes de operaciones se usan para programar, en procedimientos organizados, las instrucciones a ser ejecutadas por una máquina, para realizar distintos tipos de operaciones. Para poder importar procedimientos existentes realizados en un lenguaje de operaciones dado, en frameworks de programación específicos para el diseño de operaciones, es necesario contar con traductores entre el lenguaje en que ha sido programado el procedimiento, y el lenguaje interno utilizado por el framework de diseño. La construcción de estos traductores puede ser automatizada si se establece un mapeo entre producciones equivalentes de las gramáticas de los lenguajes fuente y destino. Las producciones se pueden considerar equivalentes cuando a pesar de tener una sintaxis diferente, tienen la misma intención semántica y pueden ser equiparadas con un árbol de sintaxis abstracta en común. Debido a ciertas diferencias sintácticas, en ocasiones este árbol sintáctico común no puede ser hallado directamente. Esto se vuelve posible, sin embargo, al realizar en primer lugar ciertas transformaciones a los programas. Este artículo presenta un acercamiento específico a la construcción de reglas de transformación para resolver estas diferencias.Abstract:Operations languages are used to program the procedures that instruct machinery to perform specific operations. To import procedures in a given operations language, into a dedicated tool for designing operations, language translators are needed to translate those procedures to the design tool's internal operations language. The construction of these translators can be automated by mapping equivalent productions in the grammar of source and target language. Productions are considered equivalent when, in spite of a differing syntax, they have the same intended semantics and can be matched to a same abstract syntax tree. For some corresponding productions, due to syntactic differences, such a common abstract syntax tree cannot be found directly. However, they can be made to match by performing some specific program transformations first. This paper presents a specific approach to write transformation rules to resolve such mismatches.

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Kraus, Stefan, Dennis Toddenroth, Martin Staudigel, et al. "Mapping the Entire Record—An Alternative Approach to Data Access from Medical Logic Modules." Applied Clinical Informatics 11, no. 02 (2020): 342–49. http://dx.doi.org/10.1055/s-0040-1709708.

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Abstract Objectives This study aimed to describe an alternative approach for accessing electronic medical records (EMRs) from clinical decision support (CDS) functions based on Arden Syntax Medical Logic Modules, which can be paraphrased as “map the entire record.” Methods Based on an experimental Arden Syntax processor, we implemented a method to transform patient data from a commercial patient data management system (PDMS) to tree-structured documents termed CDS EMRs. They are encoded in a specific XML format that can be directly transformed to Arden Syntax data types by a mapper natively integrated into the processor. The internal structure of a CDS EMR reflects the tabbed view of an EMR in the graphical user interface of the PDMS. Results The study resulted in an architecture that provides CDS EMRs in the form of a network service. The approach enables uniform data access from all Medical Logic Modules and requires no mapping parameters except a case number. Measurements within a CDS EMR can be addressed with straightforward path expressions. The approach is in routine use at a German university hospital for more than 2 years. Conclusion This practical approach facilitates the use of CDS functions in the clinical routine at our local hospital. It is transferrable to standard-compliant Arden Syntax processors with moderate effort. Its comprehensibility can also facilitate teaching and development. Moreover, it may lower the entry barrier for the application of the Arden Syntax standard and could therefore promote its dissemination.

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Poongodi, D., and G. Tholkkappia Arasu. "Multi-Agent based Sequence Algorithm for Detecting Plagiarism and Clones in Java Source Code using Abstract Syntax Tree." International Journal of Computer Applications 90, no. 15 (2014): 19–24. http://dx.doi.org/10.5120/15796-4494.

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Tan, Xiaobo, Yingjie Xu, Tong Wu, and Bohan Li. "Detection of Reflected XSS Vulnerabilities Based on Paths-Attention Method." Applied Sciences 13, no. 13 (2023): 7895. http://dx.doi.org/10.3390/app13137895.

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Cross-site scripting vulnerability (XSS) is one of the most frequently exploited and harmful vulnerabilities among web vulnerabilities. In recent years, many researchers have used different machine learning methods to detect network attacks, but these methods have not achieved high accuracy and recall rates and cannot effectively combat XSS attacks. Designing a model that can achieve high accuracy and truly proactive defense against reflected XSS vulnerabilities has become a top priority for maintaining user network security at this stage. In this paper, we propose a detection model for reflected XSS vulnerabilities based on the paths-attention method (PATS model). Firstly, the model converts vulnerability data into an intermediate representation of abstract syntax trees, then traverses the abstract syntax tree to generate multiple sets of syntactic paths, and then converts them into vector representations through word embedding matrices. The model extracts semantic features using attention mechanisms to improve training effectiveness by assigning appropriate weights to different sets of syntactic paths as it learns with neural networks, which realizes the transformation from passive defense to active defense. Additionally, in the dataset processing section, we point out the shortcomings of current research datasets and construct a reliable dataset composed of 1000 malicious samples from NIST and 10,000 benign samples from GitHub for experimentation purposes. Experimental results show that compared with other machine learning models, the paths-attention method can achieve an accuracy rate of 90.25% and F1-score of 81.62%, while reducing the training time by half to 30 h.

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Lou, Ping, Jun Chen, Li Ming Lu, et al. "An Automatic Transformation Technical Solution for Behavior Control Scripting Languages." Advanced Materials Research 971-973 (June 2014): 2023–27. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.2023.

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This paper proposed an automatic transformation technical solution for behavior control scripting languages. The solution automatically converts VBA scripting language into JavaScript scripting language. It implements automatic transformation by analyzing and comparing differences in the use of different grammatical of VBA scripts and JavaScript scripts such as data types, control structures, form controls and add-ins Etc. To ensure the accuracy and completeness of conversion the solution generates an abstract syntax tree which represents call and transitive relation between functions. Compared with the conversion tools the solution can reduce repeated manpower and improve productivity.

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Ma, Da, Xingyu Chen, Ruisheng Cao, Zhi Chen, Lu Chen, and Kai Yu. "Relation-Aware Graph Transformer for SQL-to-Text Generation." Applied Sciences 12, no. 1 (2021): 369. http://dx.doi.org/10.3390/app12010369.

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Generating natural language descriptions for structured representation (e.g., a graph) is an important yet challenging task. In this work, we focus on SQL-to-text, a task that maps a SQL query into the corresponding natural language question. Previous work represents SQL as a sparse graph and utilizes a graph-to-sequence model to generate questions, where each node can only communicate with k-hop nodes. Such a model will degenerate when adapted to more complex SQL queries due to the inability to capture long-term and the lack of SQL-specific relations. To tackle this problem, we propose a relation-aware graph transformer (RGT) to consider both the SQL structure and various relations simultaneously. Specifically, an abstract SQL syntax tree is constructed for each SQL to provide the underlying relations. We also customized self-attention and cross-attention strategies to encode the relations in the SQL tree. Experiments on benchmarks WikiSQL and Spider demonstrate that our approach yields improvements over strong baselines.

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Xu, Yingjie, Gengran Hu, Lin You, and Chengtang Cao. "A Novel Machine Learning-Based Analysis Model for Smart Contract Vulnerability." Security and Communication Networks 2021 (August 10, 2021): 1–12. http://dx.doi.org/10.1155/2021/5798033.

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In recent years, a lot of vulnerabilities of smart contracts have been found. Hackers used these vulnerabilities to attack the corresponding contracts developed in the blockchain system such as Ethereum, and it has caused lots of economic losses. Therefore, it is very important to find out the potential problems of the smart contracts and develop more secure smart contracts. As blockchain security events have raised more important issues, more and more smart contract security analysis methods have been developed. Most of these methods are based on traditional static analysis or dynamic analysis methods. There are only a few methods that use emerging technologies, such as machine learning. Some models that use machine learning to detect smart contract vulnerabilities cost much time in extracting features manually. In this paper, we introduce a novel machine learning-based analysis model by introducing the shared child nodes for smart contract vulnerabilities. We build the Abstract-Syntax-Tree (AST) for smart contracts with some vulnerabilities from two data sets including SmartBugs and SolidiFI-benchmark. Then, we build the Abstract-Syntax-Tree (AST) of the labeled smart contract for data sets named Smartbugs-wilds. Next, we get the shared child nodes from both of the ASTs to obtain the structural similarity, and then, we construct a feature vector composed of the values that measure structural similarity automatically to build our machine learning model. Finally, we get a KNN model that can predict eight types of vulnerabilities including Re-entrancy, Arithmetic, Access Control, Denial of Service, Unchecked Low Level Calls, Bad Randomness, Front Running, and Denial of Service. The accuracy, recall, and precision of our KNN model are all higher than 90%. In addition, compared with some other analysis tools including Oyente and SmartCheck, our model has higher accuracy. In addition, we spent less time for training .

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Cruz, da, Rangel Henriques, and João Varanda. "Constructing program animations using a pattern based approach." Computer Science and Information Systems 4, no. 2 (2007): 97–114. http://dx.doi.org/10.2298/csis0702097c.

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The aim of this paper is to discuss how our pattern-based strategy for the visualization of data and control flow can effectively be used to animate the program and exhibit its behavior. That result allows us to propose its use for Program Comprehension. The animator uses well known compiler techniques to inspect the source code in order to extract the necessary information to visualize it and understand program execution. We convert the source program into an internal decorated (or attributed) abstract syntax tree and then we visualize the structure by traversing it, and applying visualization rules at each node according to a pre-defined rule-base. In order to calculate the next step in the program execution, a set of rewriting rules are applied to the tree. The visualization of this new tree is shown and the program animation is constructed using an iterative process. No changes are made in the source code, and the execution is simulated step by step. Several examples of visualization are shown to illustrate the approach and support our idea of applying it in the context of a Program Comprehension environment.

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Hosseinpour, Sahereh, Mir Mohammad Reza Alavi Milani, and Hüseyin Pehlivan. "A Step-by-Step Solution Methodology for Mathematical Expressions." Symmetry 10, no. 7 (2018): 285. http://dx.doi.org/10.3390/sym10070285.

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In this paper, we propose a methodology for the step-by-step solution of problems, which can be incorporated into a computer algebra system. Our main aim is to show all the intermediate evaluation steps of mathematical expressions from the start to the end of the solution. The first stage of the methodology covers the development of a formal grammar that describes the syntax and semantics of mathematical expressions. Using a compiler generation tool, the second stage produces a parser from the grammar description. The parser is used to convert a particular mathematical expression into an Abstract Syntax Tree (AST), which is evaluated in the third stage by traversing al its nodes. After every evaluation of some nodes, which corresponds to an intermediate solution step of the related expression, the resulting AST is transformed into the corresponding mathematical expression and then displayed. Many other algebra-related issues such as simplification, factorization, distribution and substitution can be covered by the solution methodology. We currently focuses on the solutions of various problems associated with the subject of derivative, equations, single variable polynomials, and operations on functions. However, it can easily be extended to cover the other subjects of general mathematics.

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Elmounadi, Abdelali, Naoual Berbiche, Nacer Sefiani, and Nawfal El Moukhi. "ADM-Based Hybrid Model Transformation for Obtaining UML Models from PHP Code." International Journal of Recent Contributions from Engineering, Science & IT (iJES) 7, no. 1 (2019): 32. http://dx.doi.org/10.3991/ijes.v7i1.10052.

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In this paper, we present a hybrid-based model transformation, according to the Architecture Driven Modernization (ADM) approach, intended for getting UML (Unified Modeling Language) models from the PHP (Hypertext Preprocessor) code. This latter has been done by offering a tool support for automated generation of UML platform independent models from PHP ASTM (Abstract Syntax Tree Metamodel) representations, which are specific platform models. The model transformation rules are expressed in ATL (Atlas Transformation Language), which is a widely used model transformation language based on the hybrid approach. This work aims to fill the gap between the web-based applications maintenance, especially PHP-based implementations, and the model transformation processes in the ADM context.

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Journal articles on the topic 'Abstract syntax tree'

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