通过尖端深度学习技术深入探索软件缺陷和自我承认的技术债务
Sajid Ullah1, M Irfan Uddin1, Muhammad Adnan1
1Institute of Computing, Kohat University of Science Technology, Kohat, Khyber Pakhtunkhwa, Pakistan.
PloS one
|June 11, 2025
概括
这项研究引入了一种新的深度学习方法,可以同时识别自我承认的技术债务 (SATD) 和软件缺陷. 该方法增强了缺陷本地化和软件质量评估.
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
- 机器学习 机器学习
背景情况:
- 现有的研究往往单独地解决自认技术债务 (SATD) 或错误检测问题.
- 目前的方法经常忽略了用于并发SATD和缺陷分析的高级深度学习技术.
- 需要采用综合方法,以全面理解和管理软件质量问题.
研究的目的:
- 开发和评估一种基于深度学习的创新方法,用于同时识别和分类SATD和软件缺陷.
- 提高与SATD相关的软件评论中的缺陷的理解和本地化.
- 通过统一的方法改进软件质量评估和维护流程.
主要方法:
- 使用深度学习架构,包括LSTM,BI-LSTM,GRU,BI-GRU和变压器模型 (BERT,GPT-3).
- 在像Apache,Mozilla Firefox和Eclipse这样的软件存储库中的各种数据集上训练模型.
- 采用数据分析,预处理和机器学习技术进行模型开发和评估.
- 与GPT-3集成的转移学习,以提高性能.
主要成果:
- 深度学习模型实现了高性能指标,准确度和精度约为0.98.
- 变压器模型,特别是GPT-3,表现出略高的性能,达到0.984.4的精度.
- 使用GPT-3转移学习的结果是准确率为0.96和F1-Score为0.96,优于其他模型.
- 提出的方法显著改进了现有的方法.
结论:
- 开发的深度学习方法有效地同时识别和分类SATD和软件缺陷.
- 该方法对软件工程具有重大意义,可以改善质量评估,维护优先级和资源分配.
- 这项研究有助于创建更可靠,更可持续的软件系统.
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