Iterative process to improve GQM models with metrics thresholds to detect high-risk files

Naohiko Tsuda, Masaki Takada, Hironori Washizaki, Yoshiaki Fukazawa, Shunsuke Sugimura, Yuichiro Yasuda, Masanao Futakami

    研究成果: Conference contribution

    抄録

    Manual code inspections are intense and time-consuming activities to improve the maintainability and reusability of source code. Although automatic detection of high-risk source code file by metrics thresholds can help inspectors, determining the optimal thresholds is difficult Thus, we propose an iterative process to define and improve GQM models with metrics thresholds to detect high-risk files Our process clarifies experts' viewpoints in the inspection and the measurement metrics using the GQM method, define how to interpret the metrics values, searches concrete thresholds for a specific project by supervised learning using some of the file in the project as training data, and analyzes how to improve models and thresholds. We implemented our tool in R language and evaluated our process using a industrial project. Small-sized embedded C++ systems require only a few training data.

    本文言語English
    ホスト出版物のタイトルProceedings of the 2016 IEEE Region 10 Conference, TENCON 2016
    出版社Institute of Electrical and Electronics Engineers Inc.
    ページ3813-3816
    ページ数4
    ISBN(電子版)9781509025961
    DOI
    出版ステータスPublished - 2017 2 8
    イベント2016 IEEE Region 10 Conference, TENCON 2016 - Singapore, Singapore
    継続期間: 2016 11 222016 11 25

    Other

    Other2016 IEEE Region 10 Conference, TENCON 2016
    国/地域Singapore
    CitySingapore
    Period16/11/2216/11/25

    ASJC Scopus subject areas

    • コンピュータ サイエンスの応用
    • 電子工学および電気工学

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