| Item type |
SIG Technical Reports(1) |
| 公開日 |
2022-03-04 |
| タイトル |
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タイトル |
Investigating the impact of source code metrics on merge conflict resolution judgement model |
| タイトル |
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言語 |
en |
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タイトル |
Investigating the impact of source code metrics on merge conflict resolution judgement model |
| 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 |
運用・保守 |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_18gh |
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資源タイプ |
technical report |
| 著者所属 |
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Osaka University |
| 著者所属 |
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Osaka University |
| 著者所属 |
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Osaka University |
| 著者所属 |
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Osaka University |
| 著者所属(英) |
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en |
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Osaka University |
| 著者所属(英) |
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en |
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Osaka University |
| 著者所属(英) |
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en |
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Osaka University |
| 著者所属(英) |
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en |
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Osaka University |
| 著者名 |
Mohan, Bian
Tetsuya, Kanda
Kazumasa, Shimari
Katsuro, Inoue
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| 著者名(英) |
Mohan, Bian
Tetsuya, Kanda
Kazumasa, Shimari
Katsuro, Inoue
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| 論文抄録 |
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内容記述タイプ |
Other |
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内容記述 |
In large-scale software development, a version control system is frequently used. However, if multiple persons change the same piece of code in parallel, conflicts may occur. In order to merge the changes successfully, the developer must investigate the cause, re-edit the code. This can take hours or even days, delaying the project's development schedule while the developer repeatedly reviews to identify the reason for the conflict and find a solution. In the previous research, a machine learning model was created to determine how to solve merge conflicts from meta information such as the number of lines of merge conflicts, the date and time when commits were created, and the developers who created them. In this research, by adding source code metrics to the model, we aim to create a model that suggest how to resolve appropriate merge conflicts with higher accuracy. |
| 論文抄録(英) |
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内容記述タイプ |
Other |
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内容記述 |
In large-scale software development, a version control system is frequently used. However, if multiple persons change the same piece of code in parallel, conflicts may occur. In order to merge the changes successfully, the developer must investigate the cause, re-edit the code. This can take hours or even days, delaying the project's development schedule while the developer repeatedly reviews to identify the reason for the conflict and find a solution. In the previous research, a machine learning model was created to determine how to solve merge conflicts from meta information such as the number of lines of merge conflicts, the date and time when commits were created, and the developers who created them. In this research, by adding source code metrics to the model, we aim to create a model that suggest how to resolve appropriate merge conflicts with higher accuracy. |
| 書誌レコードID |
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収録物識別子タイプ |
NCID |
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収録物識別子 |
AN10112981 |
| 書誌情報 |
研究報告ソフトウェア工学（SE）
巻 2022-SE-210,
号 21,
p. 1-8,
発行日 2022-03-04
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| ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
2188-8825 |
| Notice |
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SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. |
| 出版者 |
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言語 |
ja |
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出版者 |
情報処理学会 |
IPSJ-SE22210021.pdf (1.6 MB)
