相关实验视频
一种基于双编码器的转移学习方法,用于在软件要求句子对中的自动冲突检测
Yizheng Wang1, Tao Jiang1, Jinyan Bai1
1School of Mathematics and Computer Science, Yunnan Minzu University, Kunming, China.
PloS one
|March 12, 2026
概括
这项研究引入了一个新的框架,用于检测软件需求冲突,提高不平衡数据的准确性,并加强跨领域转移学习,以实现更可靠的软件工程. 该方法显著提高了检测性能.
科学领域:
- 软件工程 软件工程 软件工程
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 确保大规模软件要求文档 (RD) 的一致性对于项目成功至关重要.
- 现有的自动冲突检测方法与不平衡的数据,有限的语义提取和差的跨域传输扎.
- 挑战包括精度低,单个编码器的局限性和无效的转移学习.
研究的目的:
- 提出一个可转移的软件需求冲突检测框架 (TSRCDF-SS),解决当前的局限性.
- 增强语义提取和提高跨领域转移学习能力.
- 在检测要求不一致方面实现更高的准确性和效率.
主要方法:
- 使用两个独立的编码器,句子-BERT (SBERT) 和简单的对比句子嵌入 (SimCSE),用于强大的句子嵌入.
- 实施一个六要素连接策略,用于组合特征表示.
- 增强了一个前神经网络 (FFNN) 分类器,具有混合损失函数,包括焦点损失,域约束和信任惩罚.
- 整合顺序和跨领域的转移学习.
主要成果:
- 在非跨域条件下,在宏观F1和加权F1得分中实现了4.9%至12.1%的改善.
- 在最佳跨领域场景下,在宏观F1中显示了平均6%的增强.
- 在检测软件要求冲突方面,超越了先进的经典方法.
结论:
- 拟议的TSRCDF-SS框架有效地解决了自动化软件需求冲突检测方面的挑战.
- 双编码器方法和增强的分类器显著提高了准确性和转移学习能力.
- 该框架为保持软件要求一致性提供了更有效和可靠的解决方案.
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