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检索条件"主题词=Abstract Syntax Tree"

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Mining Patterns from Genetic Improvement Experiments 6

Mining Patterns from Genetic Improvement Experiments

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IEEE/ACM 6th International Workshop on Genetic Improvement (GI@ICSE)

作者： Krauss, Oliver Mossenbock, Hanspeter Affenzeller, Michael Johannes Kepler Univ Linz Linz Austria Univ Appl Sci Upper Austria Wels Austria

ISBN: (纸本)9781728122687

When conducting genetic improvement experiments, a large amount of individuals (approximate to population size * generations) is created and evaluated. The corresponding experiments contain valuable data concerning the fitness of individuals for the defined criteria, such as run-time performance, memory use or robustness. This publication presents an approach to utilize this information in order to identify recurring context independent patterns in abstract syntax trees (ASTs). These patterns can be applied for restricting the search space (in the form of anti-patterns) or for grafting operators in the population. Future work includes an evaluation of this approach, as well as extending it with wildcards and class hierarchies for larger and more generalized patterns.

关键词： Genetic Improvement abstract syntax tree Pattern Mining Frequent Subgraph Mining

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KNOD: Domain Knowledge Distilled tree Decoder for Automated Program Repair 23

KNOD: Domain Knowledge Distilled Tree Decoder for Automated ...

引用

45th IEEE/ACM International Conference on Software Engineering (ICSE)

作者： Jiang, Nan Lutellier, Thibaud Lou, Yiling Tan, Lin Goldwasser, Dan Zhang, Xiangyu Purdue Univ W Lafayette IN 47907 USA Univ Alberta Edmonton AB Canada Fudan Univ Shanghai Peoples R China

ISBN: (纸本)9781665457019

Automated Program Repair (APR) improves software reliability by generating patches for a buggy program automatically. Recent APR techniques leverage deep learning (DL) to build models to learn to generate patches from existing patches and code corpora. While promising, DL-based APR techniques suffer from the abundant syntactically or semantically incorrect patches in the patch space. These patches often disobey the syntactic and semantic domain knowledge of source code and thus cannot be the correct patches to fix a bug. We propose a DL-based APR approach KNOD, which incorporates domain knowledge to guide patch generation in a direct and comprehensive way. KNOD has two major novelties, including (1) a novel three-stage tree decoder, which directly generates abstract syntax trees of patched code according to the inherent tree structure, and (2) a novel domain-rule distillation, which leverages syntactic and semantic rules and teacher-student distributions to explicitly inject the domain knowledge into the decoding procedure during both the training and inference phases. We evaluate KNOD on three widely-used benchmarks. KNOD fixes 72 bugs on the Defects4J v1.2, 25 bugs on the QuixBugs, and 50 bugs on the additional Defects4J v2.0 benchmarks, outperforming all existing APR tools.

关键词： Automated Program Repair abstract syntax tree Deep Learning

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Codewebs: Scalable Homework Search for Massive Open Online Programming Courses 14

Codewebs: Scalable Homework Search for Massive Open Online P...

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23rd International Conference on World Wide Web (WWW)

作者： Nguyen, Andy Piech, Christopher Huang, Jonathan Guibas, Leonidas Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781450327442

Massive open online courses (MOOCs), one of the latest internet revolutions have engendered hope that constant iterative improvement and economies of scale may cure the "cost disease" of higher education. While scalable in many ways, providing feedback for homework submissions (particularly open-ended ones) remains a challenge in the online classroom. In courses where the student-teacher ratio can be ten thousand to one or worse, it is impossible for instructors to personally give feedback to students or to understand the multitude of student approaches and pitfalls. Organizing and making sense of massive collections of homework solutions is thus a critical web problem. Despite the challenges, the dense solution space sampling in highly structured homeworks for some MOOCs suggests an elegant solution to providing quality feedback to students on a massive scale. We outline a method for decomposing online homework submissions into a vocabulary of "code phrases", and based on this vocabulary, we architect a queryable index that allows for fast searches into the massive dataset of student homework submissions. To demonstrate the utility of our homework search engine we index over a million code submissions from users worldwide in Stanford's Machine Learning MOOC and (a) semi-automatically learn shared structure amongst homework submissions and (b) generate specific feedback for student mistakes. Codewebs is a tool that leverages the redundancy of densely sampled, highly structured homeworks in order to force-multiply teacher effort. Giving articulate, instant feedback is a crucial component of the online learning process and thus by building a homework search engine we hope to take a step towards higher quality free education.

关键词： massive open online course MOOC education code search student feedback abstract syntax tree AST canonicalization semantic equivalence Octave Coursera

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Test coverage analysis for Object Oriented programs - Structural testing through Aspect Oriented instrumentation

Test coverage analysis for Object Oriented programs - Struct...

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2nd International Conference on Software and Data Technologies

作者： Baldini, Fabrizio Bucci, Giacomo Grassi, Leonardo Vicario, Enrico Univ Florence Dept Sistemi & Informat I-50139 Florence Italy

ISBN: (纸本)9789898111050

The introduction of Object Oriented Technologies in test centered processes has emphasized the importance of finding new methods for software verification. Testing metrics and practices, developed for structured programs, have to be adapted in order to address the prerogatives of object oriented programming. In this work, we introduce a new approach to structural coverage evaluation in the testing of OO software. Data flow paradigm is adopted and reinterpreted through the definition of a new type of structure, used to record def/use information for test critical class member variables. In the final part of this paper, we present a testing tool that employs this structure for code based coverage analysis of Java and C++ programs.

关键词： data flow analysis Object Oriented testing abstract syntax tree Aspect Oriented Programming

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Cross-language Source Code Clone Detection Based On Graph Neural Network 24

Cross-language Source Code Clone Detection Based On Graph Ne...

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3rd International Conference on Cryptography, Network Security and Communication Technology (CNSCT)

作者： Zhang, Yuguo Yang, Jia Ruan, Ou Hubei Univ Technol Wuhan Hubei Peoples R China

ISBN: (纸本)9798400716959

Code clone detection has important applications in the security field, including vulnerability mining and recovery, malware detection, and copyright protection. Currently, clone detection in a single programming language has been widely researched. However, the research on cross-language clone detection is limited, and the syntax and structure of different programming languages are not the same, which makes cross-language clone detection more difficult. In this paper, we compute the embeddings by training the graph neural network, which is a numerical vector that is based on the AST graph of each function, and then perform similarity detection by measuring the embedding distance between two functions. We implement a model called CrossLCE. To train our network, we used the CLCDSA dataset, which contains multiple programming languages (C/C++, Python, and Java.). We evaluated our approach to this dataset and gave promising results in detecting similarities between code snippets.

关键词： Clone detection Graph neural network abstract syntax tree cross-language

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JSAC: A Novel Framework to Detect Malicious JavaScript via CNNs over AST and CFG

JSAC: A Novel Framework to Detect Malicious JavaScript via C...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Jiang, Hongliang Yang, Yuxing Sun, Lu Jiang, Lin Beijing Univ Posts & Telecommun Scool Comp Sci Beijing Peoples R China

ISBN: (纸本)9781728119854

JavaScript (JS) is a dominant programming language in web/mobile development, while it is also notoriously abused by attackers due to its powerful characteristics, e.g., dynamic, prototype-based and multi-paradigm, which foil most static and dynamic analysis approaches. To detect malicious JS instances, several machine learning-based methods have been developed recently. However, these methods took JS as a natural language instead of a programming one, which can not capture its syntactic and semantic features. In this paper, we present JSAC, a novel framework to detect JS malware. It combines deep learning and program analysis techniques to capture the syntactic and semantic features of JS programs. Specifically, to get a JS program's syntactic information, we build its abstract syntax tree and employ a tree-based convolutional neural network (CNN) to extract features from it. To get its semantic information, we construct its control flow graph and feed it to another graph-based CNN. Last, the features extracted from two CNNs are fused for final detection. Evaluation on a corpus of 69,523 JS files indicates that JSAC outperforms 4 other models with 98.73% F1-score in detecting JS malware.

关键词： Malicious JavaScript CNN abstract syntax tree Control Flow Graph

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Python Source Code De-Anonymization Using Nested Bigrams 18

Python Source Code De-Anonymization Using Nested Bigrams

引用

18th IEEE International Conference on Data Mining Workshops (ICDMW)

作者： Hozhabrierdi, Pegah Hitos, Dunai Fuentes Mohan, Chilukuri K. Syracuse Univ Dept EECS Syracuse NY 13244 USA Matilock Inc Huelva Spain

ISBN: (纸本)9781538692882

An important issue in cybersecurity is the insertion or modification of code by individuals other than the original authors of the code. This motivates research on authorship attribution of unknown source code. We have addressed the deficiencies of previously used feature extraction methods and propose a novel approach: Nested Bigrams. Such features are easy to extract and carry substantial information about the interconnections between the nodes of the abstract syntax tree. We also show that for large number of authors, a Strongly Regularized Feed-forward Neural Network outperforms the Random Forest Classifier used in many code stylometric studies. A new ranking system for reducing the number of features is also proposed, and experiments show that this approach can reduce the feature set to 98 nested bigrams while maintaining a classification accuracy above 90 percent.

关键词： source code de-anonymization source code stylometry abstract syntax tree feature extraction feature ranking

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Low-Complexity Code Clone Detection using Graph-based Neural Networks 18

Low-Complexity Code Clone Detection using Graph-based Neural...

引用

18th IEEE International Conference on Mobility, Sensing and Networking (MSN)

作者： Liu, Hu Zhao, Hui Han, Changhao Hou, Lu Beijing Univ Posts & Telecommun Intelligent Comp & Commun Lab Key Lab Univ Wireless Commun Beijing 100876 Peoples R China

ISBN: (纸本)9781665464574

Code clone detection is of great significance for intellectual property protection and software maintenance. Deep learning has been applied in some research and achieved better performance than traditional methods. To adapt to more application scenarios and improve the detection efficiency, this paper proposes a low-complex code clone detection with the graph-based neural network. As the input of the neural network, code features are represented by abstract syntax trees (ASTs), in which the redundant edges are removed. The operation of pruning avoids interference in the message passing of the network and reduces the size of the graph. Then, the graph pairs for the code clone detection are sent into the message passing neural networks (MPNN). In addition, the gated recurrent unit (GRU) is used to learn the information between graph pairs to avoid the operation of Graph mapping. After multiple iterations, the attention mechanism is used to read out the graph vector, and the cosine similarity is calculated on the graph vector to obtain the code similarity. Through the experiments on two datasets, the results show that the proposed clone detection scheme removes about 20% of the redundant edges and reduces 25% of model weights, 16% of multiply-accumulate operations (MACs). In the end, the proposed method effectively reduces the training time of graph neural network while presenting a similar performance to the baseline network.

关键词： Code clone detection loss computational load abstract syntax tree graph neural network

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A Framework for Scanning Privacy Information based on Static Analysis 22

A Framework for Scanning Privacy Information based on Static...

引用

22nd IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Zhao, Yuan Yi, Gaolei Liu, Fan Hui, Zhanwei Zhao, Jianhua Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Mil Acad Sci Beijing Peoples R China

ISBN: (纸本)9781665477048

Modern software brings many conveniences to users through big data, but it also risks privacy leakage. In recent years, privacy leaks have been frequent, and various countries have introduced privacy protection bills to protect users' privacy security and avoid misuse of their private data. The researchers have conducted many studies to protect user privacy, including privacy policy compliance checks and mobile application permission checks. However, little existing work considers the verification of matching software code behavior and privacy policy. In this paper, we propose a set of privacy scanning methods to solve mentioned issues with static code analysis. We first classify privacy text and extracts privacy information. Then we perform static analysis on the code to obtain variable privacy information and privacy propagation paths by combining an abstract syntax tree and the call graph. We also match the results to the text analysis results. The experiments demonstrate that our method outperforms other classification methods in privacy text judgment, with an accuracy rate of 90% in detecting privacy information in the code. Meanwhile, the short running time ensures that no extra overhead is imposed on the user.

关键词： Static Analysis Privacy Call Graph abstract syntax tree

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Interactive Characterization of A Code Pattern 13

Interactive Characterization of A Code Pattern

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13th International Conference on Intelligent Software Methodologies, Tools, and Techniques (SoMeT)

作者： Nakayama, Ken Sakai, Eko Kobayakawa, Michihiro Tsuda Coll Kodaira Tokyo 1878577 Japan Otani Univ Kyoto Japan Tokyo Metropolitan Coll Ind Technol Tokyo Japan

ISBN: (纸本)9781614994343;9781614994336

There are semantic chunks and relations (code patterns) of various granularities in source code. Since a code pattern is closely tied to a unit of understanding by a programmer, searching specific code pattern through unfamiliar souce code would help understanding the code. Conventional string search tools, however, are not suitable for this purpose because, for instance, they do not assume the syntax of a programming language. In this paper, a tool for search-by-example through abstract syntax tree is presented. A programmer gives sets of desired and undesired nodes, then the system presents some candidate nodes resembling desired ones. The method is supervised incremental learning of decision trees.

关键词： source code search search by example abstract syntax tree

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