| Item type |
Symposium(1) |
| 公開日 |
2024-09-10 |
| タイトル |
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タイトル |
Exploring Bug Fixing Time through Causal and Preliminary Topic Analysis |
| タイトル |
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言語 |
en |
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タイトル |
Exploring Bug Fixing Time through Causal and Preliminary Topic Analysis |
| 言語 |
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言語 |
eng |
| キーワード |
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主題Scheme |
Other |
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主題 |
ポスター論文 |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_5794 |
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資源タイプ |
conference paper |
| 著者所属 |
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Waseda University |
| 著者所属 |
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Waseda University |
| 著者所属 |
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Waseda University |
| 著者所属 |
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Hitachi, Ltd. |
| 著者所属 |
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Hitachi, Ltd. |
| 著者所属 |
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Hitachi, Ltd. |
| 著者所属(英) |
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en |
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Waseda University |
| 著者所属(英) |
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en |
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Waseda University |
| 著者所属(英) |
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en |
|
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Waseda University |
| 著者所属(英) |
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en |
|
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Hitachi, Ltd. |
| 著者所属(英) |
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|
en |
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Hitachi, Ltd. |
| 著者所属(英) |
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en |
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Hitachi, Ltd. |
| 著者名 |
Sien, Reeve O. Peralta
Hironori, Washizaki
Yoshiaki, Fukawaza
Yuki, Noyori
Shuhei, Nojiri
Hideyuki, Kanuka
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| 著者名(英) |
Sien, Reeve O. Peralta
Hironori, Washizaki
Yoshiaki, Fukawaza
Yuki, Noyori
Shuhei, Nojiri
Hideyuki, Kanuka
|
| 論文抄録 |
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内容記述タイプ |
Other |
|
内容記述 |
This study investigates the determinants of Bug Fixing Time (BFT) using Bayesian Networks (BN), Linear Non-Gaussian Acyclic Models (LiNGAM), and topic modeling. Significant factors identified include 'Reporter Reputation,' 'Severity,' and 'Blocker' status. Analysis of 500 topics within bug reports revealed moderate diversity and notable BFT differences among similar topics. For instance, 132 topics were fixed within a day, yet some reports within these topics were resolved after a month, highlighting challenges in the relationship between topic modeling and BFT. These insights help prioritize key factors affecting BFT and enhance bug-fixing efficiency. Integrating these findings into causal inference models clarifies their relationships with other factors, improving software development operations. |
| 論文抄録(英) |
|
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内容記述タイプ |
Other |
|
内容記述 |
This study investigates the determinants of Bug Fixing Time (BFT) using Bayesian Networks (BN), Linear Non-Gaussian Acyclic Models (LiNGAM), and topic modeling. Significant factors identified include 'Reporter Reputation,' 'Severity,' and 'Blocker' status. Analysis of 500 topics within bug reports revealed moderate diversity and notable BFT differences among similar topics. For instance, 132 topics were fixed within a day, yet some reports within these topics were resolved after a month, highlighting challenges in the relationship between topic modeling and BFT. These insights help prioritize key factors affecting BFT and enhance bug-fixing efficiency. Integrating these findings into causal inference models clarifies their relationships with other factors, improving software development operations. |
| 書誌情報 |
ソフトウェアエンジニアリングシンポジウム2024論文集
巻 2024,
p. 311-312,
発行日 2024-09-10
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| 出版者 |
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言語 |
ja |
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出版者 |
情報処理学会 |
IPSJ-SES2024049.pdf (271.0 kB)
