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Graph collaborative filtering-based bug triaging☆
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AI
DOI：10.1016/j.jss.2023.111667.png)
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Abstract
En 中文
Issue tracking systems are widely used for collecting bug reports. A target of intelligent software engineering is to automate assigning bugs to appropriate developers. Recently, the momentum of artificial intelligence has brought many successful studies that triage bugs by classifying their reports with NLP-based methods. Some studies also try to introduce context information to represent developers. Nevertheless, they take a fundamental assumption that developers and bugs, closely related entities in real-world scenarios, should be modeled independently. To capture the bug-developer correlations in bug triaging activities, we propose a Graph Collaborative filtering-based Bug Triaging framework: (1) bug-developer correlations are modeled as a bipartite graph; (2) natural language processing-based pre-training is implemented on bug reports to initialize bug nodes; (3) spatial-temporal graph convolution strategy is designed to learn the representation of developer nodes; (4) information retrieval-based classifier is proposed to match bugs and developers. Extensive experiments across mainstream datasets show the competence of our GCBT. Moreover, We believe that GCBT could generally benefit the modeling of correlations in other software engineering scenarios. (c) 2023 Elsevier Inc. All rights reserved.
Keywords:
Software reliability engineering
Bug triaging
Deep graph learning
Graph collaborative filtering
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