Machine Learning Approaches for Authorship Attribution using Source Code Stylometry

被引：3

作者：

Frankel, Sophia F. ^{[1
]}

Ghosh, Krishnendu ^{[1
]}

机构：

[1] Coll Charleston, Dept Comp Sci, Charleston, SC 29401 USA

来源：

2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | 2021年

关键词：

Authorship Attribution; Stylometry; Source Code; Machine Learning; PLAGIARISM DETECTION;

D O I：

10.1109/BigData52589.2021.9671332

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Identification of source code authorship is vital for attribution. In this work, a machine learning framework is described to identify source code authorship. The framework integrates the features extracted using natural language processing based approaches and abstract syntax tree of the code. We evaluate the methodology on Google Code Jam dataset. We present the performance measures of the logistic regression and deep learning on the dataset.

引用

页码：3298 / 3304

页数：7

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