基于深度学习的缺陷分诊系统
Deep Learning-based Bug Triage System
查看机构详情
- OLAS Team INRIA/University of Bologna（INRIA/博洛尼亚大学 OLAS 团队）
机构由 AI 辅助整理，请以论文原文为准。
浏览论文内容
中文总结 AI 辅助
本文提出基于RoBERTa-base的自动化缺陷分诊系统，利用深度上下文表示分类缺陷报告，五轮训练内达到0.90准确率，展现高效性能。
中文摘要 AI 辅助
有效的缺陷分诊对于通过准确分类和分配报告的软件缺陷来简化软件开发周期至关重要。在本文中，我们提出了一种基于预训练的RoBERTa-base transformer架构的自动化缺陷分诊系统。通过利用深度上下文表示，我们的方法高效地对传入的缺陷报告进行分类以优化分配。实验评估表明，所提出的系统在仅五个训练周期内就达到了0.90的强缺陷识别准确率。这些发现凸显了微调后的transformer模型在实际软件工程自动化中的高效性和高性能。
英文摘要
Effective bug triage is crucial for streamlining the software development lifecycle by accurately categorizing and assigning reported software defects. In this paper, we propose an automated bug triage system built upon the pre-trained RoBERTa-base transformer architecture. By leveraging deep contextual representations, our approach efficiently classifies incoming bug reports to optimize assignment. Experimental evaluation demonstrates that the proposed system achieves a strong bug identification accuracy of 0.90 within just five training epochs. These findings highlight the efficiency and high performance of fine-tuned transformer models for practical software engineering automation.
