源代码作者接近自然语言处理

Proceedings of the 19th International Conference on Computer Systems and Technologies Pub Date : 2018-09-13 DOI:10.1145/3274005.3274031

Juraj Petrík, D. Chudá

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引用次数: 5

摘要

本文提出了一种基于现代自然语言处理方法的源代码作者归属方法。我们基于文本嵌入卷积递归神经网络的方法在一个数据集中的500位作者中达到94.5%的准确率，优于许多最先进的作者归属模型。我们的方法是像处理自然语言文本一样处理源代码，因此它潜在地独立于编程语言，具有更多的未来改进潜力。

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Source code authorship approaches natural language processing

This paper proposed method for source code authorship attribution using modern natural language processing methods. Our method based on text embedding with convolutional recurrent neural network reaches 94.5% accuracy within 500 authors in one dataset, which outperformed many state of the art models for authorship attribution. Our approach is dealing with source code as with natural language texts, so it is potentially programming language independent with more potential of future improving.

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Proceedings of the 19th International Conference on Computer Systems and Technologies

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