TY - GEN
T1 - Assessing software quality of agile student projects by data-mining software repositories
AU - Koetter, Falko
AU - Kochanowski, Monika
AU - Kintz, Maximilien
AU - Kersjes, Benedikt
AU - Bogicevic, Ivan
AU - Wagner, Stefan
N1 - Publisher Copyright:
Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.
PY - 2019
Y1 - 2019
N2 - Group student software projects are important in computer science education. Students are encouraged to self-organize and learn technical skills, preparing them for real life software development. However, the projects contribute to multiple learning objectives, making coaching students a time consuming task. Thus, it is important to have a suitable best practice development process. For providing better insights for the students, the resulting software has to be of value and meet quality requirements, including maintainability, as in real life software development. Using source code quality metrics and by data mining repository data like commit history, we analyze six student projects, measuring their quality and identifying contributing factors to success or failure of a student project. Based on the findings, we formulate recommendations to improve future projects for students and researchers alike.
AB - Group student software projects are important in computer science education. Students are encouraged to self-organize and learn technical skills, preparing them for real life software development. However, the projects contribute to multiple learning objectives, making coaching students a time consuming task. Thus, it is important to have a suitable best practice development process. For providing better insights for the students, the resulting software has to be of value and meet quality requirements, including maintainability, as in real life software development. Using source code quality metrics and by data mining repository data like commit history, we analyze six student projects, measuring their quality and identifying contributing factors to success or failure of a student project. Based on the findings, we formulate recommendations to improve future projects for students and researchers alike.
KW - Data-mining
KW - Metrics
KW - Project-based learning
KW - Software development
KW - Software quality
KW - Student project
UR - http://www.scopus.com/inward/record.url?scp=85067110425&partnerID=8YFLogxK
U2 - 10.5220/0007688602440251
DO - 10.5220/0007688602440251
M3 - Conference contribution
AN - SCOPUS:85067110425
T3 - CSEDU 2019 - Proceedings of the 11th International Conference on Computer Supported Education
SP - 244
EP - 251
BT - CSEDU 2019 - Proceedings of the 11th International Conference on Computer Supported Education
A2 - Lane, H.
A2 - Zvacek, Susan
A2 - Uhomoibhi, James
PB - SciTePress
T2 - 11th International Conference on Computer Supported Education, CSEDU 2019
Y2 - 2 May 2019 through 4 May 2019
ER -