Ties between mined structural patterns in program and their identifier names

Yoshiki Mashima, Sachio Hirokawa, Kazuhiro Takeuchi

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

    1 Citation (Scopus)

    Abstract

    Identifier names in readable and maintainable source codes are always descriptive. These names are given based on the implicit knowledge of experienced programmers. In this paper, we propose a structural pattern mining method based on support vector machines (SVM) for source codes. We extract 1,000 method names in object-oriented source codes collected from online software repositories and create 1,000 datasets labeled by positive and negative class. The structural features used for the input feature vectors to the SVM learning are designed for representing partial characteristics in the abstract syntax tree (AST) parsed from a source code. Applying this method, we made an F1 score list of the 1,000 method names, which shows the degree of patterning of each name, by using our structural features. From the list, we confirmed structural patterns were strongly associated with specific method names. A qualitative evaluation of method names was also conducted by mapping the structural feature vector of each program example to the two-dimensional plane in the same way as a previous major study. From the evaluation, we confirmed that the contrasting structure among the programs corresponds to the names given to programs. Furthermore, we show examples of visualization of structural patterns using structural features extracted by feature selection.

    Original languageEnglish
    Title of host publicationIntegrated Uncertainty in Knowledge Modelling and Decision Making - 7th International Symposium, IUKM 2019, Proceedings
    EditorsHirosato Seki, Masahiro Inuiguchi, Canh Hao Nguyen, Van-Nam Huynh
    PublisherSpringer Verlag
    Pages335-346
    Number of pages12
    ISBN (Print)9783030148140
    DOIs
    Publication statusPublished - Jan 1 2019
    Event7th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2019 - Nara, Japan
    Duration: Mar 27 2019Mar 29 2019

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume11471 LNAI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference7th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2019
    Country/TerritoryJapan
    CityNara
    Period3/27/193/29/19

    All Science Journal Classification (ASJC) codes

    • Theoretical Computer Science
    • Computer Science(all)

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