Yousofvand, Leila; Soleimani, Seyfollah and Rafe, Vahid. 2023. Automatic bug localization using a combination of deep learning and model transformation through node classification. Software Quality Journal, 31(4), pp. 1045-1063. ISSN 0963-9314 [Article]

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Abstract or Description

Bug localization is the task of automatically locating suspicious commands in the source code. Many automated bug localization approaches have been proposed for reducing costs and speeding up the bug localization process. These approaches allow developers to focus on critical commands. In this paper, we propose to treat the bug localization problem as a node classification problem. As in the existing training sets, where whole graphs are labeled as buggy and bug-free, it is required first to label all nodes in each graph. To do this, we use the Gumtree algorithm, which labels the nodes by comparing the buggy graphs with their corresponding fixed graphs. In classification, we propose to use a type of graph neural networks (GNNs), GraphSAGE. The used dataset for training and testing is JavaScript buggy code and their corresponding fixed code. The results demonstrate that the proposed method outperforms other related methods.

Item Type:	Article
Identification Number (DOI):	https://doi.org/10.1007/s11219-023-09625-5
Additional Information:	“This version of the article has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s11219-023-09625-5. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms”
Data Access Statement:	Dataset for this research is included in Dinella et al. (2020) https://openreview.net/forum?id=SJeqs6EFvB.
Keywords:	Deep learning, Bug localization, Node classification, Graph neural networks
Departments, Centres and Research Units:	Computing
Dates:	Date Event 17 February 2023 Accepted 24 March 2023 Published Online December 2023 Published
Item ID:	33380
Date Deposited:	19 Apr 2023 15:35
Last Modified:	24 Mar 2024 02:28
Peer Reviewed:	Yes, this version has been peer-reviewed.
URI:	https://research.gold.ac.uk/id/eprint/33380

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