Human-Machine Co-Boosted Bug Report Identification with Mutualistic Neural Active Learning

ArXi:2604.18862v1 Announce Type: cross 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