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基于变压器的模型用于SE研究的承诺和危险
Yan Xiao1, Xinyue Zuo2, Xiaoyue Lu1
1Shenzhen Campus of Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, Shenzhen, 518107, Guangdong, China.
概括
本研究分析了代码的变压器模型,发现仅用编码器的模型擅长执行一般任务,而仅用解码器的模型则擅长生成代码. 性能各不相同,不太受欢迎的模型有时会表现出色,一般化仍然是一个挑战.
科学领域:
- 计算机科学 计算机科学
- 软件工程 软件工程 软件工程
背景情况:
- 基于变压器的预训练模型越来越多地用于与代码相关的任务.
- 需要对这些模型的性能,资源消耗和概括进行全面分析.
研究的目的:
- 实证地分析519篇关于变压器模型代码的论文 (2017-2023年).
- 评估模型架构 (CodeBERT,CodeGPT,CodeT5) 在关键软件工程任务 (修复错误,检测错误,代码总结,代码搜索) 上.
- 评估资源消耗和概括能力.
主要方法:
- 对519篇论文进行了系统的文献审查.
- 使用CodeBERT,CodeGPT和CodeT5.5进行实证实验.
- 在四个软件工程任务中进行评估.
主要成果:
- 只有编码器模型 (CodeBERT) 在一般任务上可以超过编码器解码器模型.
- 只有解码器的模型 (CodeGPT) 对特定的生成任务显示出希望.
- 不那么受欢迎的模型可能会提供更高的性能.
- 对于理解任务,CodeBERT的效率很高;对于生成,CodeT5的效率是不一致的.
- 在受欢迎的Bug Fixing和Code Summarization基准测试中观察到较差的模型通用化.
结论:
- 变压器模型具有巨大的潜力,但面临着实际挑战.
- 模型架构的选择是关键的,并且取决于任务.
- 通用化仍然是改进代码情报研究的关键领域.
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