Bilateral Dependency Neural Networks for Cross-Language Algorithm Classification

Nghi, D Q Bui; Yu, Yijun; Jiang, Lingxiao

doi:10.1109/saner.2019.8667995

Cited by 26 publications

(13 citation statements)

References 46 publications

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“…Chen et al [12] proposed a tree-to-tree approach to transform programs from one language into another [12]. Bui et al [52] proposed a bilateral model of two encoders, each of which is for encoding the abstract syntax of code in one programming language. Bui et al [53] proposed to improve program translation via mining API mappings across programming languages based on adversarial learning.…”

Section: Cross-language Source Code Analysismentioning

confidence: 99%

Cross-Language Binary-Source Code Matching with Intermediate Representations

Gui¹,

Wan²,

Zhang³

et al. 2022

Preprint

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Binary-source code matching plays an important role in many security and software engineering related tasks such as malware detection, reverse engineering and vulnerability assessment. Currently, several approaches have been proposed for binary-source code matching by jointly learning the embeddings of binary code and source code in a common vector space. Despite much effort, existing approaches target on matching the binary code and source code written in a single programming language. However, in practice, software applications are often written in different programming languages to cater for different requirements and computing platforms. Matching binary and source code across programming languages introduces additional challenges when maintaining multi-language and multi-platform applications. To this end, this paper formulates the problem of cross-language binary-source code matching, and develops a new dataset for this new problem. We present a novel approach XLIR, which is a Transformer-based neural network by learning the intermediate representations for both binary and source code. To validate the effectiveness of XLIR, comprehensive experiments are conducted on two tasks of cross-language binary-source code matching, and cross-language source-source code matching, on top of our curated dataset. Experimental results and analysis show that our proposed XLIR with intermediate representations significantly outperforms other state-of-the-art models in both of the two tasks.

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Section: Cross-language Source Code Analysismentioning

confidence: 99%

Cross-Language Binary-Source Code Matching with Intermediate Representations

Gui¹,

Wan²,

Zhang³

et al. 2022

Preprint

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“…However, treating source code as a natural language may overlook its various unique properties, leading to a large number of recent works proposing to represent source code in structural representations, such as Abstract Syntax Tree (AST) [34,13,16,15] or Graph [31,2,6,23,49,48,53]. These models have been shown to be effective on a wide range of software engineering tasks, including code classification and bug prediction [36,17,38,44], predicting bugs [56,30,32,59], translating programs [16,21,7,8,35], etc.…”

Section: Related Workmentioning

confidence: 99%

Learning to Represent Programs with Code Hierarchies

Nguyen¹,

Bui²

2022

Preprint

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When used to process source code, graph neural networks have been shown to produce impressive results for a wide range of software engineering tasks. Existing techniques, however, still have two issues: (1) long-term dependency and (2) different code components are treated as equals when they should not be. To address these issues, we propose a method for representing code as a hierarchy (Code Hierarchy), in which different code components are represented separately at various levels of granularity. Then, to process each level of representation, we design a novel network architecture, HIRGAST, which combines the strengths of Heterogeneous Graph Transformer Networks and Tree-based Convolutional Neural Networks to learn Abstract Syntax Trees enriched with code dependency information. We also propose a novel pretraining objective called Missing Subtree Prediction to complement our Code Hierarchy. The evaluation results show that our method significantly outperforms other baselines in three downstream tasks: any-code completion, code classification, and code clone detection.Preprint. Under review.

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“…dl-based models: dl models such as ann [223,224], dnn [99,365], and rnn with Reverse neural network [342] are also employed extensively. Bui et al [58] and Bui et al [57] combined neural networks for ml models training. Specifically, Bui et al [58] built a Bilateral neural network on top of two underlying sub-networks, each of which encodes syntax and semantics of code in one language.…”

Section: Model Trainingmentioning

confidence: 99%

“…Bui et al [58] and Bui et al [57] combined neural networks for ml models training. Specifically, Bui et al [58] built a Bilateral neural network on top of two underlying sub-networks, each of which encodes syntax and semantics of code in one language. Bui et al [57] constructed BiTBCNNs-a combination layer of sub-networks to encode similarities and differences among code structures in different languages.…”

Section: Model Trainingmentioning

confidence: 99%

A Survey on Machine Learning Techniques for Source Code Analysis

Sharma¹,

Kechagia²,

Georgiou³

et al. 2021

Preprint

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Context:The advancements in machine learning techniques have encouraged researchers to apply these techniques to a myriad of software engineering tasks that use source code analysis such as testing and vulnerabilities detection. A large number of studies poses challenges to the community to understand the current landscape. Objective: We aim to summarize the current knowledge in the area of applied machine learning for source code analysis. Method: We investigate studies belonging to twelve categories of software engineering tasks and corresponding machine learning techniques, tools, and datasets that have been applied to solve them. To do so, we carried out an extensive literature search and identified 364 primary studies published between 2002 and 2021. We summarize our observations and findings with the help of the identified studies. Results: Our findings suggest that the usage of machine learning techniques for source code analysis tasks is consistently increasing. We synthesize commonly used steps and the overall workflow for each task, and summarize the employed machine learning techniques. Additionally, we collate a comprehensive list of available datasets and tools useable in this context. Finally, we summarize the perceived challenges in this area that include availability of standard datasets, reproducibility and replicability, and hardware resources. CCS Concepts: • Software and its engineering → Software libraries and repositories; Software maintenance tools; Software post-development issues; Maintaining software; • Computing methodologies → Machine learning.

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Bilateral Dependency Neural Networks for Cross-Language Algorithm Classification

Cited by 26 publications

References 46 publications

Cross-Language Binary-Source Code Matching with Intermediate Representations

Cross-Language Binary-Source Code Matching with Intermediate Representations

Learning to Represent Programs with Code Hierarchies

A Survey on Machine Learning Techniques for Source Code Analysis

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