Mutualistic Neural Active Learning Framework for Automated Bug Report Identification
A novel framework named Mutualistic Neural Active Learning (MNAL) has been launched to streamline the identification of bug reports from GitHub repositories through collaboration between humans and machines. This system tackles the difficulties arising from the increasing complexity and sheer number of bug reports, which often result in laborious and resource-heavy manual processing. MNAL integrates a neural language model that learns from and generalizes reports across various projects with active learning strategies. This synergy fosters a beneficial partnership between machine learners (the neural language model) and human labelers (developers) to enhance the knowledge base. The framework aims to bolster software quality maintenance by efficiently identifying bug reports and directing them to the right resolution teams. The research was shared on arXiv with the identifier arXiv:2604.18862v1, categorized as a cross announcement.
Key facts
- Mutualistic Neural Active Learning (MNAL) is a cross-project framework for automated bug report identification
- The system addresses challenges from increasing complexity and volume of bug reports
- MNAL uses human-machine collaboration to boost identification effectiveness
- The framework combines neural language models with active learning techniques
- Neural language models learn and generalize reports across different projects
- Active learning forms neural active learning within the system
- MNAL creates mutualistic relationships between machine learners and human labelers
- The research was announced on arXiv under identifier arXiv:2604.18862v1
Entities
Institutions
- GitHub
- arXiv