TY - GEN
T1 - A Front-End Framework Selection Assistance System with Customizable Quantification Indicators Based on Analysis of Repository and Community Data
AU - Kiyokawa, Koichi
AU - Jin, Qun
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Front-end frameworks are in increasing demand in web application development. However, it is difficult to compare them manually because of their rapid evolution and big variety. Prior research has revealed several indicators that developers consider important when selecting a framework. In this study, we propose and develop a system that assists developers in the selection process of a front-end framework, which collects data from repository and user community, such as GitHub and other sources, and quantifies a set of indicators. This system is built as a web application, and users can specify the importance of an indicator by adjusting the weight for each indicator. As a result of semi-structured interviews with front-end developers after using our system based on practical scenarios, we found that the proposed system is effective for narrowing down the framework and has practicality.
AB - Front-end frameworks are in increasing demand in web application development. However, it is difficult to compare them manually because of their rapid evolution and big variety. Prior research has revealed several indicators that developers consider important when selecting a framework. In this study, we propose and develop a system that assists developers in the selection process of a front-end framework, which collects data from repository and user community, such as GitHub and other sources, and quantifies a set of indicators. This system is built as a web application, and users can specify the importance of an indicator by adjusting the weight for each indicator. As a result of semi-structured interviews with front-end developers after using our system based on practical scenarios, we found that the proposed system is effective for narrowing down the framework and has practicality.
KW - Front-end framework
KW - Repository mining
KW - Semi-structured interview
KW - Technology selection
UR - http://www.scopus.com/inward/record.url?scp=85126265346&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85126265346&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-96600-3_4
DO - 10.1007/978-3-030-96600-3_4
M3 - Conference contribution
AN - SCOPUS:85126265346
SN - 9783030965990
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 41
EP - 55
BT - Big-Data-Analytics in Astronomy, Science, and Engineering - 9th International Conference on Big Data Analytics, BDA 2021, Proceedings
A2 - Sachdeva, Shelly
A2 - Watanobe, Yutaka
A2 - Bhalla, Subhash
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th International Conference on Big Data Analytics, BDA 2021
Y2 - 7 December 2021 through 9 December 2021
ER -