Multi-triage: A multi-task learning framework for bug triage

Aung, TWW; Wan, Y; Huo, H; Sui, Y

Multi-triage: A multi-task learning framework for bug triage

Aung, TWW Wan, Y Huo, H

Sui, Y

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Journal of Systems and Software, 2022, 184, pp. 111133
Issue Date:: 2022-02-01

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Field	Value	Language
dc.contributor.author	Aung, TWW
dc.contributor.author	Wan, Y
dc.contributor.author	Huo, H https://orcid.org/0000-0003-2440-714X
dc.contributor.author	Sui, Y https://orcid.org/0000-0002-9510-6574
dc.date.accessioned	2022-01-27T03:11:21Z
dc.date.available	2022-01-27T03:11:21Z
dc.date.issued	2022-02-01
dc.identifier.citation	Journal of Systems and Software, 2022, 184, pp. 111133
dc.identifier.issn	0164-1212
dc.identifier.uri	http://hdl.handle.net/10453/153631
dc.description.abstract	Assigning developers and allocating issue types are two important tasks in the bug triage process. Existing approaches tackle these two tasks separately, which is time-consuming due to repetition of effort and negating the values of correlated information between tasks. In this paper, a multi-triage model is proposed that resolves both tasks simultaneously via multi-task learning (MTL). First, both tasks can be regarded as a classification problem, based on historical issue reports. Second, performances on both tasks can be improved by jointly interpreting the representations of the issue report information. To do so, a text encoder and abstract syntax tree (AST) encoder are used to extract the feature representation of bug descriptions and code snippets accordingly. Finally, due to the disproportionate ratio of class labels in training datasets, the contextual data augmentation approach is introduced to generate syntactic issue reports to balance the class labels. Experiments were conducted on eleven open-source projects to demonstrate the effectiveness of this model compared with state-of-the-art methods.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Journal of Systems and Software
dc.relation.isbasedon	10.1016/j.jss.2021.111133
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0803 Computer Software, 0804 Data Format, 0806 Information Systems
dc.subject.classification	Software Engineering
dc.title	Multi-triage: A multi-task learning framework for bug triage
dc.type	Journal Article
utslib.citation.volume	184
utslib.for	0803 Computer Software
utslib.for	0804 Data Format
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2024-02-28T00:00:00+1000Z
dc.date.updated	2022-01-27T03:11:20Z
pubs.publication-status	Accepted
pubs.volume	184

Abstract:

Assigning developers and allocating issue types are two important tasks in the bug triage process. Existing approaches tackle these two tasks separately, which is time-consuming due to repetition of effort and negating the values of correlated information between tasks. In this paper, a multi-triage model is proposed that resolves both tasks simultaneously via multi-task learning (MTL). First, both tasks can be regarded as a classification problem, based on historical issue reports. Second, performances on both tasks can be improved by jointly interpreting the representations of the issue report information. To do so, a text encoder and abstract syntax tree (AST) encoder are used to extract the feature representation of bug descriptions and code snippets accordingly. Finally, due to the disproportionate ratio of class labels in training datasets, the contextual data augmentation approach is introduced to generate syntactic issue reports to balance the class labels. Experiments were conducted on eleven open-source projects to demonstrate the effectiveness of this model compared with state-of-the-art methods.

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http://hdl.handle.net/10453/153631