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8th International Conference on Data Science in Cyberspace, DSC 2023

作者： Yang, Yizu Lee, Yuzhang He, Zeyu Wang, Chunlu Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350331035

Abstract Syntax Tree (AST) is a graph structure obtained from code syntax analysis whose representation has recently played an increasingly important role in program classification. In our study, we innovatively propose that a better AST representation should be hierarchical, combined with both low-and-high level features of the program in AST. Low-level program features in AST are mainly affected by code and writing style. Higher-level program features, such as the structure and logic of the program, are constituted by code blocks as program codes in the same code block share similar program logic features. However, it is difficult to cluster codes into code blocks by general code analysis. To efficiently extract hierarchical AST representations, we apply a graph coarsening (pooling) module to AST for the first time, which can coarsen and cluster the original AST nodes into several node clusters based on node features and structures. After graph coarsening, these node clusters are considered code blocks, and the whole coarsened AST graph representation will represent higher-level program features. Our experimental results show that hierarchical AST representations yield an improvement of about 2% -3% and 12%-17% accuracy of cross-language program classification on Dataset JC and Dataset Leetcode, respectively, compared to the AST representations only based on low-level program features. © 2023 IEEE.

关键词： Graph Coarsening Hierarchical AST Representations program classification

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Unified Abstract Syntax Tree Representation Learning for Cross-Language program classification 30

Unified Abstract Syntax Tree Representation Learning for Cro...

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30th IEEE/ACM International Conference on program Comprehension (ICPC)

作者： Wang, Kesu Yan, Meng Zhang, He Hu, Haibo Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Chongqing Univ Sch Big Data & Software Engn Chongqing Peoples R China

ISBN: (纸本)9781450392983

program classification can be regarded as a high-level abstraction of code, laying a foundation for various tasks related to source code comprehension, and has a very wide range of applications in the field of software engineering, such as code clone detection, code smell classification, defects classification, etc. The cross-language program classification can realize code transfer in different programming languages, and can also promote cross-language code reuse, thereby helping developers to write code quickly and reduce the development time of code transfer. Most of the existing studies focus on the semantic learning of the code, whilst few studies are devoted to cross-language tasks. The main challenge of cross-language program classification is how to extract semantic features of different programming languages. In order to cope with this difficulty, we propose a Unified Abstract Syntax Tree (namely UAST in this paper) neural network. In detail, the core idea of UAST consists of two unified mechanisms. First, UAST learns an AST representation by unifying the AST traversal sequence and graph-like AST structure for capturing semantic code features. Second, we construct a mechanism called unified vocabulary, which can reduce the feature gap between different programming languages, so it can achieve the role of cross-language program classification. Besides, we collect a dataset containing 20,000 files of five programming languages, which can be used as a benchmark dataset for the cross-language program classification task. We have done experiments on two datasets, and the results show that our proposed approach out-performs the state-of-the-art baselines in terms of four evaluation metrics (Precision, Recall, F1-score, and Accuracy).

关键词： program Comprehension program classification Code Representation Learning Cross-language program classification

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Automating program Structure classification 21

Automating Program Structure Classification

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Proceedings of the 52nd ACM Technical Symposium on Computer Science Education

作者： Will Crichton Georgia Gabriela Sampaio Pat Hanrahan Stanford University Stanford CA USA

ISBN: (纸本)9781450380621

When students write programs, their program structure provides insight into their learning process. However, analyzing program structure by hand is time-consuming, and teachers need better tools for computer-assisted exploration of student solutions. As a first step towards an education-oriented program analysis toolkit, we show how supervised machine learning methods can automatically classify student programs into a predetermined set of high-level structures. We evaluate two models on classifying student solutions to the Rainfall problem: a nearest-neighbors classifier using syntax tree edit distance and a recurrent neural network. We demonstrate that these models can achieve 91% classification accuracy when trained on 108 programs. We further explore the generality, trade-offs, and failure cases of each model.

关键词： program classification neural networks machine learning

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A New program classification Method Based on Binary Instrumentation and Instruction Flow Feature Extraction 10

A New Program Classification Method Based on Binary Instrume...

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10th International Conference on Broadband and Wireless Computing, Communication and Applications

作者： Cui, Baojiang Cao, Mengchen Chen, Shilei Qi, Weikong Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China Natl Engn Lab Mobile Network Secur Beijing Peoples R China CAST Inst Telecommun Satellite Beijing Peoples R China

ISBN: (纸本)9781467383158

With the development of Internet technology, more and more unknown programs appears in the network environment, the detection and classification technology is increasingly becoming an important technology in the field of information security. This paper presents a new method of program classification using binary instrumentation, dynamic instruction flow feature extraction, auto feature selection and Naive Bayes classifier technology. Finally we use two-class classification, five categories, and twenty-class classification experiments to prove the correctness of the method and present the future direction for improvement of the method.

关键词： program classification binary instrumentation dynamic instruction flow feature extraction Naive Bayes

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Analysis of program Representations Based on Abstract Syntax Trees and Higher-Order Markov Chains for Source Code classification Task

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FUTURE INTERNET 2023年第9期15卷 314-314页

作者： Gorchakov, Artyom V. Demidova, Liliya A. Sovietov, Peter N. Russian Technol Univ Fed State Budget Educ Inst Higher Educ Inst Informat Technol MIREA 78 Vernadsky Ave Moscow 119454 Russia

In this paper we consider the research and development of classifiers that are trained to predict the task solved by source code. Possible applications of such task detection algorithms include method name prediction, hardware-software partitioning, programming standard violation detection, and semantic code duplication search. We provide the comparative analysis of modern approaches to source code transformation into vector-based representations that extend the variety of classification and clustering algorithms that can be used for intelligent source code analysis. These approaches include word2vec, code2vec, first-order and second-order Markov chains constructed from abstract syntax trees (AST), histograms of assembly language instruction opcodes, and histograms of AST node types. The vectors obtained with the forementioned approaches are then used to train such classification algorithms as k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP). The obtained results show that the use of program vectors based on first-order AST-based Markov chains with an RF-based classifier leads to the highest accuracy, precision, recall, and F1 score. Increasing the order of Markov chains considerably increases the dimensionality of a vector, without any improvements in classifier quality, so we assume that first-order Markov chains are best suitable for real world applications. Additionally, the experimental study shows that first-order AST-based Markov chains are least sensitive to the used classification algorithm.

关键词： program classification Markov chains abstract syntax trees task classification static analysis k-nearest neighbor support vector machine random forest multilayer perceptron

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Hierarchical semantic-aware neural code representation

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JOURNAL OF SYSTEMS AND SOFTWARE 2022年 191卷

作者： Jiang, Yuan Su, Xiaohong Treude, Christoph Wang, Tiantian Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China Univ Melbourne Sch Comp & Informat Syst Melbourne Vic Australia Harbin Inst Technol Sch Comp Sci & Technol 92 Xidazhi St Harbin Heilongjiang Peoples R China

Code representation is a fundamental problem in many software engineering tasks. Despite the effort made by many researchers, it is still hard for existing methods to fully extract syntactic, structural and sequential features of source code, which form the hierarchical semantics of the program and are necessary to achieve a deeper code understanding. To alleviate this difficulty, we propose a new supervised approach based on the novel use of Tree-LSTM to incorporate the sequential and the global semantic features of programs explicitly into the representation model. Unlike previous techniques, our proposed model can not only learn low-level syntactic information within each statement but also the high-level semantic information between statements over the constructed semantic graph. Besides, considering that the sequential semantics is also critical for developers to understand the dependency path and data flow transmission, we propose a DFS-based method to generate the topological order of statements being processed, and then feed them as well as their in-neighboring information and syntactic embeddings into the proposed model to learn richer statement-level semantic features. Extensive experiments on multiple program comprehension tasks, e.g., code clone detection, demonstrate that our method achieves promising performance compared with other existing baselines.

关键词： Code representation Hierarchical semantics program classification Clone detection Vulnerability detection Deep learning Graph-LSTM

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program representations for predictive compilation: State of affairs in the early 20's

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JOURNAL OF COMPUTER LANGUAGES 2022年 73卷

作者： da Silva, Anderson Faustino Borin, Edson Pereira, Fernando Magno Quintao Junior, Nilton Luiz Queiroz Napoli, Otavio Oliveira Univ Estadual Maringa Maringa Brazil Univ Estadual Campinas Campinas Brazil Univ Fed Minas Gerais Belo Horizonte Brazil

In the last five years, predictive compilation has advanced with long strides. Contributions in the field include new program embeddings, new learning architectures, and datasets with millions of programs. This paper evaluates 25 state-of-the-art program embeddings, three of them new, plus two learning models from previous work. We have trained this apparatus with three large datasets, and have applied it onto three classification problems. When classifying programs according to the problem that they solve, we reproduced the high -accuracy results seen in previous work. However, we have not been able to repeat these results in the two new classification challenges that we study: namely, determining the depth of the most nested loop in a program and determining the best sequence of optimizations to reduce code size of programs. Negative results emerged, even in spite of the large number of classifiers, 25, that we have evaluated. Surprisingly, using the histogram of instruction opcodes, a very simple program embedding, led to about the same classification accuracy than embeddings like IR2VEC or INST2VEC, which were designed to solve stochastic compilation tasks.

关键词： Predictive compilation Machine learning program representation Code embedding program classification

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Modular Tree Network for Source Code Representation Learning

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ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2020年第4期29卷 1–23页

作者： Wang, Wenhan Li, Ge Shen, Sijie Xia, Xin Jin, Zhi Peking Univ 5 Yiheyuan Rd Beijing Peoples R China Monash Univ Wellington Rd Melbourne Vic Australia

Learning representation for source code is a foundation of many program analysis tasks. In recent years, neural networks have already shown success in this area, but most existing models did not make full use of the unique structural information of programs. Although abstract syntax tree (AST)-based neural models can handle the tree structure in the source code, they cannot capture the richness of different types of substructure in programs. In this article, we propose a modular tree network that dynamically composes different neural network units into tree structures based on the input AST. Different from previous tree-structural neural network models, a modular tree network can capture the semantic differences between types of AST substructures. We evaluate our model on two tasks: program classification and code clone detection. Our model achieves the best performance compared with state-of-the-art approaches in both tasks, showing the advantage of leveraging more elaborate structure information of the source code.

关键词： Deep learning neural networks program classification code clone detection

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Getting Information Systems programs Classified as STEM: A U.S.-based Perspective from an AIS Task Force Study and Panel Discussion

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COMMUNICATIONS OF THE ASSOCIATION FOR INFORMATION SYSTEMS 2019年第1期44卷 408-420页

作者： Jones, Mary C. Srite, Mark Chandrasekaran, Ranga Iyer, Lakshmi S. Kayworth, Tim Thatcher, Jason B. Univ North Texas Informat Technol & Decis Sci Denton TX 76203 USA Univ Wisconsin Milwaukee Informat Technol Management Milwaukee WI USA Univ Illinois MIS Chicago IL USA Univ Illinois Grad Studies Chicago IL USA Appalachian State Univ Comp Informat Syst & Supply Chain Management Boone NC 28608 USA Baylor Univ Informat Syst Waco TX 76798 USA Univ Alabama Informat Syst Stat & Operat Management Tuscaloosa AL 35487 USA

Science, technology, engineering, and math (STEM) is a designation for educational degree programs that have certain benefits that non-STEM programs do not. To achieve a STEM classification, a program must meet certain criteria. programs in the natural sciences, engineering, and math departments often readily meet these criteria. However, although programs such as information systems, information technology, and business intelligence have technology at their core, these programs, especially in business colleges, often face difficulties in meeting the STEM criteria. In this paper, we review the STEM designation and provide insight into information systems and related degree programs that have received this designation based on findings from an AIS task force and an AMCIS panel discussion.

关键词： STEM Designation program classification Information Systems Business program Recruitment International Optional Practical Training

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Bilateral Dependency Neural Networks for Cross-Language Algorithm classification 26

Bilateral Dependency Neural Networks for Cross-Language Algo...

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26th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Bui, Nghi D. Q. Yu, Yijun Jiang, Lingxiao Singapore Management Univ Sch Informat Syst Singapore Singapore Open Univ Ctr Res Comp Milton Keynes Bucks England

ISBN: (纸本)9781728105918

Algorithm classification is to automatically identify the classes of a program based on the algorithm(s) and/or data structure(s) implemented in the program. It can be useful for various tasks, such as code reuse, code theft detection, and malware detection. Code similarity metrics, on the basis of features extracted from syntax and semantics, have been used to classify programs. Such features, however, often need manual selection effort and are specific to individual programming languages, limiting the classifiers to programs in the same language. To recognize the similarities and differences among algorithms implemented in different languages, this paper describes a framework of Bilateral Neural Networks (Bi-NN) that builds a neural network on top of two underlying sub-networks, each of which encodes syntax and semantics of code in one language. A whole Bi-NN can be trained with bilateral programs that implement the same algorithms and/or data structures in different languages and then be applied to recognize algorithm classes across languages. We have instantiated the framework with several kinds of token-, tree-and graph-based neural networks that encode and learn various kinds of information in code. We have applied the instances of the framework to a code corpus collected from GitHub containing thousands of Java and C++ programs implementing 50 different algorithms and data structures. Our evaluation results show that the use of Bi-NN indeed produces promising algorithm classification results both within one language and across languages, and the encoding of dependencies from code into the underlying neural networks helps improve algorithm classification accuracy further. In particular, our custom-built dependency trees with tree-based convolutional neural networks achieve the highest classification accuracy among the different instances of the framework that we have evaluated. Our study points to a possible future research direction to tailor bilateral and mu

关键词： cross-language mapping program classification algorithm classification code embedding code dependency neural network bilateral neural network

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