Accurate Developer Recommendation for Bug Resolution

Bug resolution refers to the activity that developers perform to diagnose, fix, test, and document bugs during software development and maintenance. It is a collaborative activity among developers who contribute their knowledge, ideas, and expertise to resolve bugs. Given a bug report, we would like...

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Main Authors: XIA, Xin, LO, David, WANG, Xinyu, ZHOU, Bo
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Language:English
Published: Institutional Knowledge at Singapore Management University 2013
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Online Access:https://ink.library.smu.edu.sg/sis_research/2024
https://ink.library.smu.edu.sg/context/sis_research/article/3023/viewcontent/wcre13_devrecommend.pdf
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spelling sg-smu-ink.sis_research-30232018-12-05T06:06:19Z Accurate Developer Recommendation for Bug Resolution XIA, Xin LO, David WANG, Xinyu ZHOU, Bo Bug resolution refers to the activity that developers perform to diagnose, fix, test, and document bugs during software development and maintenance. It is a collaborative activity among developers who contribute their knowledge, ideas, and expertise to resolve bugs. Given a bug report, we would like to recommend the set of bug resolvers that could potentially contribute their knowledge to fix it. We refer to this problem as developer recommendation for bug resolution. In this paper, we propose a new and accurate method named DevRec for the developer recommendation problem. DevRec is a composite method which performs two kinds of analysis: bug reports based analysis (BR-Based analysis), and developer based analysis (D-Based analysis). In the BR-Based analysis, we characterize a new bug report based on past bug reports that are similar to it. Appropriate developers of the new bug report are found by investigating the developers of similar bug reports appearing in the past. In the D-Based analysis, we compute the affinity of each developer to a bug report based on the characteristics of bug reports that have been fixed by the developer before. This affinity is then used to find a set of developers that are “close” to a new bug report. We evaluate our solution on 5 large bug report datasets including GCC, OpenOffice, Mozilla, Netbeans, and Eclipse containing a total of 107,875 bug reports. We show that DevRec could achieve recall@5 and recall@10 scores of 0.4826-0.7989, and 0.6063-0.8924, respectively. We also compare DevRec with other state-of-art methods, such as Bugzie and DREX. The results show that DevRec on average improves recall@5 and recall@10 scores of Bugzie by 57.55% and 39.39% respectively. DevRec also outperforms DREX by improving the average recall@5 and recall@10 scores by 165.38% and 89.36%, respectively. 2013-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2024 info:doi/10.1109/WCRE.2013.6671282 https://ink.library.smu.edu.sg/context/sis_research/article/3023/viewcontent/wcre13_devrecommend.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Developer Recommendation Multi-label Learning Topic Model Composite Method Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Developer Recommendation
Multi-label Learning
Topic Model
Composite Method
Software Engineering
spellingShingle Developer Recommendation
Multi-label Learning
Topic Model
Composite Method
Software Engineering
XIA, Xin
LO, David
WANG, Xinyu
ZHOU, Bo
Accurate Developer Recommendation for Bug Resolution
description Bug resolution refers to the activity that developers perform to diagnose, fix, test, and document bugs during software development and maintenance. It is a collaborative activity among developers who contribute their knowledge, ideas, and expertise to resolve bugs. Given a bug report, we would like to recommend the set of bug resolvers that could potentially contribute their knowledge to fix it. We refer to this problem as developer recommendation for bug resolution. In this paper, we propose a new and accurate method named DevRec for the developer recommendation problem. DevRec is a composite method which performs two kinds of analysis: bug reports based analysis (BR-Based analysis), and developer based analysis (D-Based analysis). In the BR-Based analysis, we characterize a new bug report based on past bug reports that are similar to it. Appropriate developers of the new bug report are found by investigating the developers of similar bug reports appearing in the past. In the D-Based analysis, we compute the affinity of each developer to a bug report based on the characteristics of bug reports that have been fixed by the developer before. This affinity is then used to find a set of developers that are “close” to a new bug report. We evaluate our solution on 5 large bug report datasets including GCC, OpenOffice, Mozilla, Netbeans, and Eclipse containing a total of 107,875 bug reports. We show that DevRec could achieve recall@5 and recall@10 scores of 0.4826-0.7989, and 0.6063-0.8924, respectively. We also compare DevRec with other state-of-art methods, such as Bugzie and DREX. The results show that DevRec on average improves recall@5 and recall@10 scores of Bugzie by 57.55% and 39.39% respectively. DevRec also outperforms DREX by improving the average recall@5 and recall@10 scores by 165.38% and 89.36%, respectively.
format text
author XIA, Xin
LO, David
WANG, Xinyu
ZHOU, Bo
author_facet XIA, Xin
LO, David
WANG, Xinyu
ZHOU, Bo
author_sort XIA, Xin
title Accurate Developer Recommendation for Bug Resolution
title_short Accurate Developer Recommendation for Bug Resolution
title_full Accurate Developer Recommendation for Bug Resolution
title_fullStr Accurate Developer Recommendation for Bug Resolution
title_full_unstemmed Accurate Developer Recommendation for Bug Resolution
title_sort accurate developer recommendation for bug resolution
publisher Institutional Knowledge at Singapore Management University
publishDate 2013
url https://ink.library.smu.edu.sg/sis_research/2024
https://ink.library.smu.edu.sg/context/sis_research/article/3023/viewcontent/wcre13_devrecommend.pdf
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