Predicting the Fixer of Software Bugs via a Collaborative Multiplex Network: Two Case Studies

Huang, Jianguo; Ma, Yutao

doi:10.1007/978-3-030-30146-0_33

Cited by 1 publication

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, a small number of researchers also attempted to leverage hidden features extracted from bug tossing graphs by network representation learning. For example, Huang et al [13] considered structural information of a multiplex DCN and then integrated the idea of network embedding to propose an automatic bug triaging approach. Alazzam et al [43] proposed a graph-based feature augmentation approach that utilizes graph partitioning based on neighborhood overlap.…”

Section: B Deep-learning-based Bug Triagingmentioning

confidence: 99%

See 1 more Smart Citation

A Spatial-Temporal Graph Neural Network Framework for Automated Software Bug Triaging

Wu,

Ma,

Xiang

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

The bug triaging process, an essential process of assigning bug reports to the most appropriate developers, is related closely to the quality and costs of software development.As manual bug assignment is a labor-intensive task, especially for large-scale software projects, many machine-learning-based approaches have been proposed to automatically triage bug reports. Although developer collaboration networks (DCNs) are dynamic and evolving in the real-world, most automated bug triaging approaches focus on static tossing graphs at a single time slice. Also, none of the previous studies consider periodic interactions among developers. To address the problems mentioned above, in this article, we propose a novel spatial-temporal dynamic graph neural network (ST-DGNN) framework, including a joint random walk (JRWalk) mechanism and a graph recurrent convolutional neural network (GRCNN) model. In particular, JRWalk aims to sample local topological structures in a graph with two sampling strategies by considering both node importance and edge importance. GRCNN has three components with the same structure, i.e., hourly-periodic, daily-periodic, and weeklyperiodic components, to learn the spatial-temporal features of dynamic DCNs. We evaluated our approach's effectiveness by comparing it with several state-of-the-art graph representation learning methods in two domain-specific tasks that belong to node classification. In the two tasks, experiments on two real-world, large-scale developer collaboration networks collected from the Eclipse and Mozilla projects indicate that the proposed approach outperforms all the baseline methods.

show abstract

Section: B Deep-learning-based Bug Triagingmentioning

confidence: 99%

“…Due to the prevalence of large-scale networked systems [11]- [13], graph nodes' latent representation (or embedding) has been widely investigated. The basic idea of graph representation learning is to learn a low-dimensional vector for each node in a graph.…”

Section: Introductionmentioning

confidence: 99%

A Spatial-Temporal Graph Neural Network Framework for Automated Software Bug Triaging

Wu,

Ma,

Xiang

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Predicting the Fixer of Software Bugs via a Collaborative Multiplex Network: Two Case Studies

Cited by 1 publication

References 29 publications

A Spatial-Temporal Graph Neural Network Framework for Automated Software Bug Triaging

A Spatial-Temporal Graph Neural Network Framework for Automated Software Bug Triaging

Contact Info

Product

Resources

About