Machine learning to evaluate evolvability defects: Code metrics thresholds for a given context

Naohiko Tsuda, Hironori Washizaki, Yoshiaki Fukazawa, Yuichiro Yasuda, Shunsuke Sugimura

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

    Abstract

    Evolvability defects are non-understandable and non-modifiable states that do not directly produce runtime behavioral failures. Automatic source code evaluation by metrics and thresholds can help reduce the burden of a manual inspection. This study addresses two problems. (1) Evolvability defects are not usually managed in bug tracking systems. (2) Conventional methods cannot fully interpret the relations among the metrics in a given context (e.g., programming language, application domain). The key actions of our method are to (1) gather trainingdata for machine learning by experts' manual inspection of some of the files in given systems (benchmark) and (2) employ a classification-tree learner algorithm, C5.0, which can deal with non-orthogonal relations between metrics. Furthermore, we experimentally confirm that, even with less training-data, our method provides a more precise evaluation than four conventional methods (the percentile, Alves' method, Bender's method, and the ROC curve-based method).

    Original languageEnglish
    Title of host publicationProceedings - 2018 IEEE 18th International Conference on Software Quality, Reliability, and Security, QRS 2018
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages83-94
    Number of pages12
    ISBN (Print)9781538677575
    DOIs
    Publication statusPublished - 2018 Aug 2
    Event18th IEEE International Conference on Software Quality, Reliability, and Security, QRS 2018 - Lisbon, Portugal
    Duration: 2018 Jul 162018 Jul 20

    Other

    Other18th IEEE International Conference on Software Quality, Reliability, and Security, QRS 2018
    CountryPortugal
    CityLisbon
    Period18/7/1618/7/20

    Keywords

    • Classification-tree
    • Contextual thresholds
    • Evolvability defects
    • Goal-Question-Metrics (GQM)
    • Machine learning
    • Software metrics

    ASJC Scopus subject areas

    • Software
    • Safety, Risk, Reliability and Quality

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  • Cite this

    Tsuda, N., Washizaki, H., Fukazawa, Y., Yasuda, Y., & Sugimura, S. (2018). Machine learning to evaluate evolvability defects: Code metrics thresholds for a given context. In Proceedings - 2018 IEEE 18th International Conference on Software Quality, Reliability, and Security, QRS 2018 (pp. 83-94). [8424960] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/QRS.2018.00022