Developer Assignment Method for Software Defects Based on Related Issue Prediction
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| Yayımlandı: | Mathematics vol. 12, no. 3 (2024), p. 425 |
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| Yazar: | |
| Diğer Yazarlar: | , , |
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MDPI AG
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| Online Erişim: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 003 | UK-CbPIL | ||
| 022 | |a 2227-7390 | ||
| 024 | 7 | |a 10.3390/math12030425 |2 doi | |
| 035 | |a 2923945268 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 084 | |a 231533 |2 nlm | ||
| 100 | 1 | |a Liu, Baochuan | |
| 245 | 1 | |a Developer Assignment Method for Software Defects Based on Related Issue Prediction | |
| 260 | |b MDPI AG |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The open-source software platform hosts a large number of software defects, and the task of relying on administrators to manually assign developers is often time consuming. Thus, it is crucial to determine how to assign software defects to appropriate developers. This paper presents DARIP, a method for assigning developers to address software defects. First, the correlation between software defects and issues is considered, predicting related issues for each defect and comprehensively calculating the textual characteristics of the defect using the BERT model. Second, a heterogeneous collaborative network is constructed based on the three development behaviors of developers: reporting, commenting, and fixing. The meta-paths are defined based on the four collaborative relationships between developers: report–comment, report–fix, comment–comment, and comment–fix. The graph-embedding algorithm metapath2vec extracts developer characteristics from the heterogeneous collaborative network. Then, a classifier based on a deep learning model calculates the probability assigned to each developer category. Finally, the assignment list is obtained according to the probability ranking. Experiments on a dataset of 20,280 defects from 9 popular projects show that the DARIP method improves the average of the Recall@5, the Recall@10, and the MRR by 31.13%, 21.40%, and 25.45%, respectively, compared to the state-of-the-art method. | |
| 653 | |a Recall | ||
| 653 | |a Algorithms | ||
| 653 | |a Collaboration | ||
| 653 | |a Defects | ||
| 653 | |a Public domain | ||
| 653 | |a Machine learning | ||
| 653 | |a Open source software | ||
| 653 | |a Software | ||
| 700 | 1 | |a Zhang, Li | |
| 700 | 1 | |a Liu, Zhenwei | |
| 700 | 1 | |a Jiang, Jing | |
| 773 | 0 | |t Mathematics |g vol. 12, no. 3 (2024), p. 425 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2923945268/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/2923945268/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2923945268/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |