Paper Info
Title
A Front-End Framework Selection Assistance System with Customizable Quantification Indicators Based on Analysis of Repository and Community Data
Abstract
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.
Year
DOI
Venue
2021
10.1007/978-3-030-96600-3_4
BIG-DATA-ANALYTICS IN ASTRONOMY, SCIENCE, AND ENGINEERING, BDA 2021
Keywords
DocType
Volume
Technology selection, Front-end framework, Repository mining, Semi-structured interview
Conference
13167
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Koichi Kiyokawa100.34
Qun Jin200.34