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A Front-End Framework Selection Assistance System with Customizable Quantification Indicators Based on Analysis of Repository and Community Data

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Big-Data-Analytics in Astronomy, Science, and Engineering (BDA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13167))

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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.

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Authors and Affiliations

  1. Graduate School of Human Sciences, Waseda University, Tokorozawa, Saitama, 3591192, Japan

    Koichi Kiyokawa & Qun Jin

Authors
  1. Koichi Kiyokawa
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  2. Qun Jin
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Corresponding author

Correspondence to Qun Jin .

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Editors and Affiliations

  1. National Institute of Technology, Delhi, India

    Shelly Sachdeva

  2. University of Aizu, Aizu-wakamatsu city, Fukushima, Japan

    Yutaka Watanobe

  3. University of Aizu, Aizu-wakamatsu city, Fukushima, Japan

    Subhash Bhalla

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Kiyokawa, K., Jin, Q. (2022). A Front-End Framework Selection Assistance System with Customizable Quantification Indicators Based on Analysis of Repository and Community Data. In: Sachdeva, S., Watanobe, Y., Bhalla, S. (eds) Big-Data-Analytics in Astronomy, Science, and Engineering. BDA 2021. Lecture Notes in Computer Science(), vol 13167. Springer, Cham. https://doi.org/10.1007/978-3-030-96600-3_4

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  • DOI: https://doi.org/10.1007/978-3-030-96600-3_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-96599-0

  • Online ISBN: 978-3-030-96600-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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