TY - GEN
T1 - How Do Contributors Impact Code Naturalness? An Exploratory Study of 50 Python Projects
AU - Bunkerd, Thanadon
AU - Wang, Dong
AU - Kula, Raula Gaikovina
AU - Ragkhitwetsagul, Chaiyong
AU - Choetkiertikul, Morakot
AU - Sunetnanta, Thanwadee
AU - Ishio, Takashi
AU - Matsumoto, Kenichi
N1 - Funding Information:
This research project was partially supported by the Faculty of Information and Communication Technology, Mahidol University and JSPS KAKENHI Grant Numbers 18H04094, JP18KT0013 and 17H00731.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Recent studies have shown how software is comparable to natural languages, meaning that source code is highly repetitive and predictable. Other studies have shown the naturalness as indicators for code quality (i.e., buggy code). With the rise of social coding and the popularity of open source projects, the software is now being built with contributions that come from contributors from diverse backgrounds. From this social contribution perspective, we explore how contributors impact code naturalness. In detail, our exploratory study investigators whether the developers' history of programming language experience affects the code naturalness. Calculating the code naturalness of 678 contributors from 50 open-source python projects, we analyze how two aspects of contributor activities impact the code naturalness: (a) the number of contributors in a software project, (b) diversity of programming language contributions. The results show that the code naturalness is affected by the diversity of contributors and that more collaborative software tends to be less predictable. This exploratory study serves as evidence into the relationship between code naturalness and the programming diversity of contributors.
AB - Recent studies have shown how software is comparable to natural languages, meaning that source code is highly repetitive and predictable. Other studies have shown the naturalness as indicators for code quality (i.e., buggy code). With the rise of social coding and the popularity of open source projects, the software is now being built with contributions that come from contributors from diverse backgrounds. From this social contribution perspective, we explore how contributors impact code naturalness. In detail, our exploratory study investigators whether the developers' history of programming language experience affects the code naturalness. Calculating the code naturalness of 678 contributors from 50 open-source python projects, we analyze how two aspects of contributor activities impact the code naturalness: (a) the number of contributors in a software project, (b) diversity of programming language contributions. The results show that the code naturalness is affected by the diversity of contributors and that more collaborative software tends to be less predictable. This exploratory study serves as evidence into the relationship between code naturalness and the programming diversity of contributors.
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U2 - 10.1109/IWESEP49350.2019.00010
DO - 10.1109/IWESEP49350.2019.00010
M3 - Conference contribution
AN - SCOPUS:85078103010
T3 - Proceedings - 2019 10th International Workshop on Empirical Software Engineering in Practice, IWESEP 2019
SP - 7
EP - 12
BT - Proceedings - 2019 10th International Workshop on Empirical Software Engineering in Practice, IWESEP 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Workshop on Empirical Software Engineering in Practice, IWESEP 2019
Y2 - 13 December 2019 through 14 December 2019
ER -