Effective prioritization of issue reports is crucial in software engineering to optimize resource allocation and address critical problems promptly. However, the manual classification of issue reports for prioritization is laborious and lacks scalability. Alternatively, many open source software (OSS) projects employ automated processes for this task, albeit relying on substantial datasets for adequate training. This research seeks to devise an automated approach that ensures reliability in issue prioritization, even when trained on smaller datasets. Our proposed methodology harnesses the power of Generative Pre-trained Transformers (GPT), recognizing their potential to efficiently handle this task. By leveraging the capabilities of such models, we aim to develop a robust system for prioritizing issue reports accurately, mitigating the necessity for extensive training data while maintaining reliability. In our research, we have developed a reliable GPT-based approach to accurately label and prioritize issue reports with a reduced training dataset. By reducing reliance on massive data requirements and focusing on few-shot fine-tuning, our methodology offers a more accessible and efficient solution for issue prioritization in software engineering. Our model predicted issue types in individual projects up to 93.2% in precision, 95% in recall, and 89.3% in F1-score.

翻译：对议题报告进行有效优先级排序对于软件工程中优化资源分配和及时解决关键问题至关重要。然而，人工对议题报告进行分类以确定优先级既耗费人力又缺乏可扩展性。作为替代方案，许多开源软件项目采用自动化流程处理此任务，但需依赖大量数据集进行充分训练。本研究旨在设计一种自动化方法，即使在小规模数据集上训练也能确保议题优先级排序的可靠性。我们提出的方法充分利用了生成式预训练Transformer（GPT）的能力，认可其在高效处理该任务中的潜力。通过此类模型的强大性能，我们致力于开发一套稳健的议题报告优先级排序系统，在保持可靠性的同时减少对大规模训练数据的依赖。在研究中，我们开发了一种基于GPT的可靠方法，能够利用缩减的训练数据集精准标注并排序议题报告。通过降低对海量数据的需求并聚焦小样本微调，我们的方法为软件工程中的议题优先级排序提供了更易获取且高效的解决方案。该模型在单个项目中预测议题类型的准确率达93.2%，召回率达95%，F1分数达89.3%。