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

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    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.

    Original languageEnglish
    Title of host publicationProceedings of the 2016 IEEE Region 10 Conference, TENCON 2016
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages3813-3816
    Number of pages4
    ISBN (Electronic)9781509025961
    DOIs
    Publication statusPublished - 2017 Feb 8
    Event2016 IEEE Region 10 Conference, TENCON 2016 - Singapore, Singapore
    Duration: 2016 Nov 222016 Nov 25

    Other

    Other2016 IEEE Region 10 Conference, TENCON 2016
    CountrySingapore
    CitySingapore
    Period16/11/2216/11/25

    Fingerprint

    Inspection
    Supervised learning
    Maintainability
    Reusability
    Concretes

    Keywords

    • GQM
    • Software Maintenance
    • Software Measurement
    • Software Reusability
    • Threshold

    ASJC Scopus subject areas

    • Computer Science Applications
    • Electrical and Electronic Engineering

    Cite this

    Tsuda, N., Takada, M., Washizaki, H., Fukazawa, Y., Sugimura, S., Yasuda, Y., & Futakami, M. (2017). Iterative process to improve GQM models with metrics thresholds to detect high-risk files. In Proceedings of the 2016 IEEE Region 10 Conference, TENCON 2016 (pp. 3813-3816). [7848777] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TENCON.2016.7848777

    Iterative process to improve GQM models with metrics thresholds to detect high-risk files. / Tsuda, Naohiko; Takada, Masaki; Washizaki, Hironori; Fukazawa, Yoshiaki; Sugimura, Shunsuke; Yasuda, Yuichiro; Futakami, Masanao.

    Proceedings of the 2016 IEEE Region 10 Conference, TENCON 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 3813-3816 7848777.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Tsuda, N, Takada, M, Washizaki, H, Fukazawa, Y, Sugimura, S, Yasuda, Y & Futakami, M 2017, Iterative process to improve GQM models with metrics thresholds to detect high-risk files. in Proceedings of the 2016 IEEE Region 10 Conference, TENCON 2016., 7848777, Institute of Electrical and Electronics Engineers Inc., pp. 3813-3816, 2016 IEEE Region 10 Conference, TENCON 2016, Singapore, Singapore, 16/11/22. https://doi.org/10.1109/TENCON.2016.7848777
    Tsuda N, Takada M, Washizaki H, Fukazawa Y, Sugimura S, Yasuda Y et al. Iterative process to improve GQM models with metrics thresholds to detect high-risk files. In Proceedings of the 2016 IEEE Region 10 Conference, TENCON 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 3813-3816. 7848777 https://doi.org/10.1109/TENCON.2016.7848777
    Tsuda, Naohiko ; Takada, Masaki ; Washizaki, Hironori ; Fukazawa, Yoshiaki ; Sugimura, Shunsuke ; Yasuda, Yuichiro ; Futakami, Masanao. / Iterative process to improve GQM models with metrics thresholds to detect high-risk files. Proceedings of the 2016 IEEE Region 10 Conference, TENCON 2016. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 3813-3816
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