| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2023-12-21 |
| タイトル |
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タイトル |
A framework for conditional statement technical debt identification and description |
| 言語 |
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言語 |
eng |
| キーワード |
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主題Scheme |
Other |
|
主題 |
Software analytics |
| キーワード |
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主題Scheme |
Other |
|
主題 |
Self-admitted technical debt |
| キーワード |
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主題Scheme |
Other |
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主題 |
Software documentation |
| キーワード |
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主題Scheme |
Other |
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主題 |
Software quality |
| キーワード |
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主題Scheme |
Other |
|
主題 |
Machine learning |
| キーワード |
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主題Scheme |
Other |
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主題 |
Conditional statements |
| 資源タイプ |
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資源タイプ |
journal article |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
Alhefdhi, Abdulaziz
Dam, Hoa Khanh
Nugroho, Yusuf Sulistyo
Hata, Hideaki
石尾, 隆
Ghose, Aditya
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| 抄録 |
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内容記述タイプ |
Abstract |
|
内容記述 |
Technical Debt occurs when development teams favour short-term operability over long-term stability. Since this places software maintainability at risk, technical debt requires early attention to avoid paying for accumulated interest. Most of the existing work focuses on detecting technical debt using code comments, known as Self-Admitted Technical Debt (SATD). However, there are many cases where technical debt instances are not explicitly acknowledged but deeply hidden in the code. In this paper, we propose a framework that caters for the absence of SATD comments in code. Our Self-Admitted Technical Debt Identification and Description (SATDID) framework determines if technical debt should be self-admitted for an input code fragment. If that is the case, SATDID will automatically generate the appropriate descriptive SATD comment that can be attached with the code. While our approach is applicable in principle to any type of code fragments, we focus in this study on technical debt hidden in conditional statements, one of the most TD-carrying parts of code. We explore and evaluate different implementations of SATDID. The evaluation results demonstrate the applicability and effectiveness of our framework over multiple benchmarks. Comparing with the results from the benchmarks, our approach provides at least 21.35, 59.36, 31.78, and 583.33% improvements in terms of Precision, Recall, F-1, and Bleu-4 scores, respectively. In addition, we conduct a human evaluation to the SATD comments generated by SATDID. In 1-5 and 0?5 scales for Acceptability and Understandability, the total means achieved by our approach are 3.128 and 3.172, respectively. |
| 書誌情報 |
en : Automated Software Engineering
巻 29,
号 2,
発行日 2022-10-03
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| artnum |
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値 |
60 |
| 出版者 |
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出版者 |
Springer |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
1573-7535 |
| 出版者版DOI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.1007/s10515-022-00364-8 |
| 出版者版URI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
URI |
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関連識別子 |
https://link.springer.com/article/10.1007/s10515-022-00364-8 |
| 権利 |
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権利情報Resource |
http://creativecommons.org/licenses/by/4.0/ |
|
権利情報 |
c The Author(s) 2022 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. |
| 著者版フラグ |
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出版タイプ |
NA |
