Human-Machine Co-Boosted Bug Report Identification with Mutualistic Neural Active Learning
Bug reports, encompassing a wide range of bug types, are crucial for maintaining software quality. However, the increasing complexity and volume of bug reports pose a significant challenge in sole manual identification and assignment to the appropriate teams for resolution, as dealing with all the reports is time-consuming and resource-intensive. In this paper, we introduce a cross-project framework, dubbed Mutualistic Neural Active Learning (MNAL), designed for automated and more effective identification of bug reports from GitHub repositories boosted by human-machine collaboration. MNAL utilizes a neural language model that learns and generalizes reports across different projects, coupl...
| Authors | Guoming Long, Shihai Wang, Hui Fang et al. |
|---|---|
| Primary category | cs.SE |
| Published | 20 April 2026 |
| Last updated at source | 20 April 2026 |
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