Background

Transfer learning allows us to train deep architectures requiring a large number of learned parameters, even if the amount of available data is limited, by leveraging existing models previously trained for another task. In previous attempts to classify image-based software artifacts in the absence of big data, it was noted that standard off-the-shelf deep architectures such as VGG could not be utilized due to their large parameter space and therefore had to be replaced by customized architectures with fewer layers. This proves to be challenging to empirical software engineers who would like to make use of existing architectures without the need for customization.

Findings

Here we explore the applicability of transfer learning utilizing models pre-trained on non-software engineering data applied to the problem of classifying software unified modeling language (UML) diagrams. Our experimental results show training reacts positively to transfer learning as related to sample size, even though the pre-trained model was not exposed to training instances from the software domain. We contrast the transferred network with other networks to show its advantage on different sized training sets, which indicates that transfer learning is equally effective to custom deep architectures in respect to classification accuracy when large amounts of training data is not available.

Conclusion

Our findings suggest that transfer learning, even when based on models that do not contain software engineering artifacts, can provide a pathway for using off-the-shelf deep architectures without customization. This provides an alternative to practitioners who want to apply deep learning to image-based classification but do not have the expertise or comfort to define their own network architectures.

中文翻译：

---

探索迁移学习在挖掘基于图像的软件工件中的功效

背景

转移学习使我们能够利用先前为其他任务训练的现有模型，来训练需要大量学习参数的深度架构，即使可用数据量有限。在先前的尝试中，在没有大数据的情况下对基于图像的软件工件进行分类时，应注意的是，由于其较大的参数空间，无法使用标准的现成的深层架构（例如VGG），因此必须替换为定制的较少层的体系结构。对于想使用现有架构而不需要定制的经验丰富的软件工程师来说，这证明是具有挑战性的。

发现

在这里，我们探讨了使用在非软件工程数据上预先训练的模型进行迁移学习的适用性，该模型适用于对软件统一建模语言（UML）图进行分类的问题。我们的实验结果表明，即使预先训练的模型没有暴露于软件领域的训练实例中，训练对与样本量相关的转移学习也有积极的反应。我们将转移的网络与其他网络进行对比，以显示其在不同规模的训练集上的优势，这表明在没有大量训练数据的情况下，转移学习对于自定义深度架构在分类准确性方面同样有效。

结论

我们的发现表明，即使基于不包含软件工程工件的模型进行迁移学习，也可以提供无需定制即可使用现成的深度架构的途径。这为想要将深度学习应用于基于图像的分类但又不具备定义自己的网络体系结构的专业知识或经验的从业者提供了一种选择。