An increasingly popular set of techniques adopted by software engineering (SE) researchers to automate development tasks are those rooted in the concept of Deep Learning (DL). The popularity of such techniques largely stems from their automated feature engineering capabilities, which aid in modeling software artifacts. However, due to the rapid pace at which DL techniques have been adopted, it is difficult to distill the current successes, failures, and opportunities of the current research landscape. In an effort to bring clarity to this cross-cutting area of work, from its modern inception to the present, this paper presents a systematic literature review of research at the intersection of SE & DL. The review canvases work appearing in the most prominent SE and DL conferences and journals and spans 84 papers across 22 unique SE tasks. We center our analysis around the components of learning, a set of principles that govern the application of machine learning techniques (ML) to a given problem domain, discussing several aspects of the surveyed work at a granular level. The end result of our analysis is a research roadmap that both delineates the foundations of DL techniques applied to SE research, and likely areas of fertile exploration for the future.

中文翻译：

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关于深度学习在软件工程研究中使用的系统文献综述

软件工程 (SE) 研究人员为自动化开发任务而采用的一组越来越流行的技术是植根于深度学习 (DL) 概念的技术。此类技术的流行很大程度上源于它们的自动化特征工程能力，这有助于对软件工件进行建模。然而，由于 DL 技术的采用速度很快，因此很难提取当前研究领域的成功、失败和机遇。为了使这一跨领域工作更加清晰，从现代开始到现在，本文对 SE 和 DL 交叉领域的研究进行了系统的文献综述。审查画布出现在最著名的 SE 和 DL 会议和期刊中，涵盖 22 项独特的 SE 任务的 84 篇论文。我们围绕学习的组成部分进行分析，这是一组控制机器学习技术 (ML) 在给定问题领域的应用的原则，并在粒度级别上讨论了所调查工作的几个方面。我们分析的最终结果是一个研究路线图，它既描绘了应用于 SE 研究的 DL 技术的基础，又描绘了未来可能的肥沃探索领域。