TY - GEN
T1 - A preliminary investigation of developer profiles based on their activities and code quality: Who does what?
AU - Gonzalez, Cristina Aguilera
AU - Zumel, Laia Albors
AU - Acero, Jesus Antonanzas
AU - Lenarduzzi, Valentina
AU - Martincz-Femandez, Silverio
AU - Rodriguez, Sonia Rabanaque
PY - 2021/3/10
Y1 - 2021/3/10
N2 - Developers work on different tasks in different conditions based on individual technical skills and personal habits. Identifying developer groups by mining their repositories is key for various tasks ranging from understanding developers types in open source projects, to help project managers concerned with the team allocation and coordination of human resources in companies. We aimed at identifying distinct groups of developer profiles based on well defined characteristics and at characterizing the most common quality issue types introduced by each profile in their code. We considered 77,932 commits of 33 open source Java projects, clustering their 2460 developers using dimensionality reduction techniques and applying the k-means algorithm. We identified five profiles among 2460 developers based on project experience, developer productivity and the common quality issues they introduce in the code. Results can be used by developer teams to detect and cope with harmful practices, in order to be more efficient by reducing the number of bugs they produce, looking for adequate training options, and balancing their teams.
AB - Developers work on different tasks in different conditions based on individual technical skills and personal habits. Identifying developer groups by mining their repositories is key for various tasks ranging from understanding developers types in open source projects, to help project managers concerned with the team allocation and coordination of human resources in companies. We aimed at identifying distinct groups of developer profiles based on well defined characteristics and at characterizing the most common quality issue types introduced by each profile in their code. We considered 77,932 commits of 33 open source Java projects, clustering their 2460 developers using dimensionality reduction techniques and applying the k-means algorithm. We identified five profiles among 2460 developers based on project experience, developer productivity and the common quality issues they introduce in the code. Results can be used by developer teams to detect and cope with harmful practices, in order to be more efficient by reducing the number of bugs they produce, looking for adequate training options, and balancing their teams.
KW - code quality
KW - developer characterisation
KW - machine learning
KW - software engineering
KW - SonarQube
U2 - 10.1109/QRS54544.2021.00103
DO - 10.1109/QRS54544.2021.00103
M3 - Article in proceedings
AN - SCOPUS:85141430282
T3 - Proceedings - IEEE International Conference on Software Quality, Reliability and Security (QRS)
SP - 938
EP - 945
BT - Proceedings - 2021 21st International Conference on Software Quality, Reliability and Security, QRS 2021
PB - IEEE
T2 - 21st International Conference on Software Quality, Reliability and Security, QRS 2021
Y2 - 6 December 2021 through 10 December 2021
ER -