Safe Rust Code Recommendation Based on Siamese Graph Neural Network

Lu, Xiuwen; Zhang, Zhicong; Xu, Hui

doi:10.1109/prdc55274.2022.00015

Cited by 1 publication

(4 citation statements)

References 19 publications

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Section: Introductionmentioning

confidence: 58%

“…This work is an extension of our previous conference paper [8] which demonstrated our preliminary results on this problem. In the conference paper [8], we only considered replaceable code pairs but not irreplaceable ones. This work systematically studies the replacement issues for different unsafe APIs and summarises the patterns that could affect the substitutability and replacement strategies.…”

Section: Introductionmentioning

confidence: 58%

“…We choose the following two existing approaches as our baseline. SGNN [8]: The work employs a siamese graph neural network to learn embeddings of unsafe code snippets and make recommendations. Different from this work, SGNN preprocesses unsafe code snippets into attributed control‐flow graphs for learning.…”

Section: Discussionmentioning

confidence: 99%

“…There are two papers similar to this work, including SGNN [8] and another work by Zhang et al [16]. SGNN is our previous conference paper with preliminary results presented.…”

Section: Related Workmentioning

confidence: 94%

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SafeNet: Towards mitigating replaceable unsafe Rust code via a recommendation‐based approach

Dong,

Zhang,

Cui

et al. 2024

Software Testing Verif & Rel

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Rust is a system‐level programming language with advantages in memory safety. It ensures that any Rust programs without unsafe code should not incur undefined behaviours. However, unsafe code still plays an essential role in Rust to achieve low‐level control. Therefore, a major design pattern of Rust programs is interior unsafe, which wraps unsafe code as safe APIs and handles all undefined behaviours internally. Rust standard library already provides a rich set of safe APIs to facilitate Rust code development. Nevertheless, due to unfamiliarity with these APIs, developers may misuse unnecessary unsafe code and suffer memory‐safety risks. In this paper, we investigate an approach to mitigate replaceable unsafe code. We first analyse unsafe APIs of the Rust standard library and summarize their common usage patterns. Each pattern corresponds to one or several code samples in our knowledge base. Then, we develop an approach to automatically recognize the usage pattern and recommend corresponding code samples. Our approach leverages dataflow analysis to exclude impossible patterns and employs a BERT‐based machine learning model to find the most similar pattern among the rest. We have conducted evaluation experiments with 472 unsafe code snippets collected from GitHub projects and successfully recognized the pattern of 394 snippets. We hope our approach can assist developers in detecting unnecessary unsafe code and suggesting safe alternatives.

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Section: Introductionmentioning

confidence: 58%

Section: Introductionmentioning

confidence: 58%

Section: Discussionmentioning

confidence: 99%

“…There are two papers similar to this work, including SGNN [8] and another work by Zhang et al [16]. SGNN is our previous conference paper with preliminary results presented.…”

Section: Related Workmentioning

confidence: 94%

See 2 more Smart Citations