Research Article
Predicting the Fixer of Software Bugs via a Collaborative Multiplex Network: Two Case Studies
@INPROCEEDINGS{10.1007/978-3-030-30146-0_33, author={Jinxiao Huang and Yutao Ma}, title={Predicting the Fixer of Software Bugs via a Collaborative Multiplex Network: Two Case Studies}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 15th EAI International Conference, CollaborateCom 2019, London, UK, August 19-22, 2019, Proceedings}, proceedings_a={COLLABORATECOM}, year={2019}, month={8}, keywords={Collaborative multiplex network Network embedding Bug fixer prediction Structure and text features}, doi={10.1007/978-3-030-30146-0_33} }- Jinxiao Huang
Yutao Ma
Year: 2019
Predicting the Fixer of Software Bugs via a Collaborative Multiplex Network: Two Case Studies
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-30146-0_33
Abstract
Bug triaging is an essential activity of defect repair, which is closely related to the cost of software maintenance. Researchers have proposed automatic bug triaging approaches to recommend bug fixers more efficiently and accurately. In addition to text features, most of the previous studies focused on single-layer bug tossing (or reassignment) graphs, but they ignored the multiplex (or multi-layer) network characteristics of human cooperative behavior. In this study, we build a collaborative multiplex network composed of a tossing graph and an e-mail communication graph in the bug triaging process. By integrating the idea of network embedding and multiplex network measures, we propose a new strategy of random walks. Moreover, we present a bug fixer prediction model that takes structure and text features as inputs. Experimental results on two large-scale open-source projects show that the proposed method outperforms the selected baseline approaches in terms of commonly-used evaluation metrics.