抄録
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%
This work has been supported by JSPS KAKENHI (Grant Number 16H05857 and 17H00731).
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詳細情報 詳細情報について
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- CRID
- 1050858784329630080
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- NII論文ID
- 120006783451
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- ISSN
- 26400715
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- HANDLE
- 10061/13964
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- 本文言語コード
- en
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- 資料種別
- conference paper
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- データソース種別
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- IRDB
- CiNii Articles