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Publication Detail
Automatic Classifying Self-Admitted Technical Debt Using N-Gram IDF
  • Publication Type:
    Conference
  • Authors:
    Wattanakriengkrai S, Srisermphoak N, Sintoplertchaikul S, Choetkiertikul M, Ragkhitwetsagul C, Sunetnanta T, Hata H, Matsumoto K
  • Publisher:
    IEEE
  • Publication date:
    02/01/2020
  • Pagination:
    316, 322
  • Published proceedings:
    Proceedings - Asia-Pacific Software Engineering Conference, APSEC
  • Volume:
    2019-December
  • ISBN-13:
    9781728146485
  • Status:
    Published
  • Name of conference:
    2019 26th Asia-Pacific Software Engineering Conference (APSEC)
  • Conference place:
    Putrajaya, Malaysia
  • Conference start date:
    02/12/2019
  • Conference finish date:
    05/12/2019
  • Print ISSN:
    1530-1362
Abstract
© 2019 IEEE. Technical Debt (TD) introduces a quality problem and increases maintenance cost since it may require improvements in the future. Several studies show that it is possible to automatically detect TD from source code comments that developers intentionally created, so-called self-admitted technical debt (SATD). Those studies proposed to use binary classification technique to predict whether a comment shows SATD. However, SATD has different types (e.g. design SATD and requirement SATD). In this paper, we therefore propose an approach using N-gram Inverse Document Frequency (IDF) and employ a multi-class classification technique to build a model that can identify different types of SATD. From the empirical evaluation on 10 open-source projects, our approach outperforms alternative methods (e.g. using BOW and TF-IDF). Our approach also improves the prediction performance over the baseline benchmark by 33%.
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