2026-09-20T10:23:55.648739800Zhttps://opus.uleth.ca/server/oai/requestoai:opus.uleth.ca:10133/58182021-01-08T07:04:55Zcom_10133_3346com_10133_1com_10133_296col_10133_3347col_10133_298
ULethbridge OPUS Repository
supervisor
Rice, Jacqueline E.
author
Alam, Sowkat
author
University of Lethbridge. Faculty of Arts and Science
2021-01-07T16:36:21Z
2021-01-07T16:36:21Z
2021
https://hdl.handle.net/10133/5818
Machine learning techniques have been widely used to understand the use of various sociolinguistic characteristics. These techniques can also be applied to analyze artificial languages. This research focuses on the influence of socio-characteristics, especially region and gender, on an artificial language (programming language). Software complexity features, 103 programming features, and their correlations (using pearson correlation) are also explored in this work. Machine learning and statistical techniques are used to determine whether any dissimilarities or similarities exist in the use of C++ programming language. We show that machine learning models can predict the region of programmers with 78.36\% accuracy and the gender of programmers with 62.63\% accuracy. We hypothesize that feature frequency difference may be a reason for lower accuracy in the gender-based program classification. We also demonstrate that some features such as for-loops and if-else conditions are closely correlated to the complexity of a computer program.
en_US
Artificial intelligence
Computer programming
Dissertations, Academic
Machine learning
Programming (Computers)
Programming languages (Computers)
Sociolinguistics
Computer program complexity and its correlation with program features and sociolinguistics
Thesis
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
URL
http://localhost:4000/bitstreams/c5ab7d49-d140-4a64-b2c7-9f788d766e93/download
File
MD5
425c16c23b41f599e687594a37a1cf77
680165
application/pdf
ALAM_SOWKAT_MSC_2021.pdf
URL
http://localhost:4000/bitstreams/d9296ed0-734f-4321-b767-23010df59146/download
File
MD5
5a6bd4db9b272d3c3b10f675c979bcd3
135378
text/plain
ALAM_SOWKAT_MSC_2021.pdf.txt