A commit change-based weighted complex network approach to identify potential fault prone classes

Chun Yong Chong, Sai Peck Lee

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    Abstract

    Over the past few years, attention has been focused on utilizing complex network analysis to gain a high-level abstraction view of software systems. While many studies have been proposed to use interactions between software components at the variable, method, class, package, or combination of multiple levels, limited studies investigated how software change history and evolution pattern can be used as a basis to model software-based weighted complex network. This paper attempts to fill in the gap by proposing an approach to model a commit change-based weighted complex network based on historical software change and evolution data captured from GitHub repositories with the aim to identify potential fault prone classes. Experiments were carried out using three open-source software to validate the proposed approach. Using the well-known change burst metric as a benchmark, the proposed method achieved average precision of 0.77 and recall of 0.8 on all the three test subjects.

    Original languageEnglish
    Title of host publicationProceedings of the 13th International Conference on Software Technologies
    EditorsLeszek Maciaszek, Marten van Sinderen
    Place of PublicationSetúbal Portugal
    PublisherScitepress
    Pages437-448
    Number of pages12
    Volume1
    ISBN (Electronic)9789897583209
    Publication statusPublished - 2019
    EventInternational Conference on Software and Data Technologies 2018 - Porto, Portugal
    Duration: 26 Jul 201828 Jul 2018
    Conference number: 13th
    https://link.springer.com/book/10.1007/978-3-030-29157-0 (Proceedings)
    http://www.icsoft.org/ (Website)

    Conference

    ConferenceInternational Conference on Software and Data Technologies 2018
    Abbreviated titleICSOFT 2018
    CountryPortugal
    CityPorto
    Period26/07/1828/07/18
    Internet address

    Keywords

    • Commit Change Data
    • Complex Network
    • Mining Software Repositories
    • Software Change Coupling
    • Software Fault Identification

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