2026-09-18T21:49:48.809169700Zhttps://opus.uleth.ca/server/oai/requestoai:opus.uleth.ca:10133/60152021-09-10T06:04:42Zcom_10133_3346com_10133_1com_10133_296col_10133_3347col_10133_298
ULethbridge OPUS Repository
supervisor
Rice, Jacqueline E.
author
Bayrami, Parinaz
author
University of Lethbridge. Faculty of Arts and Science
2021-09-09T15:20:31Z
2021-09-09T15:20:31Z
2021
https://hdl.handle.net/10133/6015
Machine learning approaches are widely used in natural language analysis. Previous research has shown that similar techniques can be applied in the analysis of computer programming (artificial) languages. In this thesis, we focus on identifying the authors of computer programs by using machine learning techniques. We extend these techniques to determine which features capture the writing style of authors in the classification of a computer program according to the author's identity. We then propose a novel approach for computer program author identification. In this method, program features from the text documents are combined with authors' sociological features (gender and region) to develop the classification model. Several experiments have been conducted on two datasets composed of computer programs written in C++, and the results are encouraging. According to the experimental results, the author's identity can be predicted with a $75\%$ accuracy rate.
en_US
Research Subject Categories::TECHNOLOGY
Artificial intelligence
Authorship
C++/CLI (Computer program language)
Code generators
Computer programming
Dissertations, Academic
Machine learning
Code authorship attribution using content-based and non-content-based features
Thesis
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URL
http://localhost:4000/bitstreams/2fbdb991-c30b-4e87-a0b7-8d0fbf050b11/download
File
MD5
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application/pdf
BAYRAMI_PARINAZ_MSC_2021.pdf
URL
http://localhost:4000/bitstreams/24f6a66c-8e86-446b-8a88-3d4315b2724f/download
File
MD5
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text/plain
BAYRAMI_PARINAZ_MSC_2021.pdf.txt