A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research
A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research
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DOI：
10.1145/3485275
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
Cody Watson;Nathan Cooper;David Nader-Palacio;Kevin Moran;D. Poshyvanyk
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文献类型：
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作者：
Cody Watson;Nathan Cooper;David Nader-Palacio;Kevin Moran;D. Poshyvanyk
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那些植根于深度学习（DL）概念的技术。这些技术的流行主要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技术的快速发展，很难提炼出当前研究领域的成功、失败和机遇。为了使这个跨领域的工作清晰，从现代开始到现在，本文提出了一个系统的文献综述的研究在SE和DL的交叉点。这篇综述涵盖了出现在最著名的SE和DL会议和期刊上的工作，涵盖了23个独特的SE任务的128篇论文。我们围绕学习的组成部分进行分析，这是一组指导机器学习技术（ML）应用于给定问题领域的原则，在粒度级别上讨论了调查工作的几个方面。我们的分析的最终结果是一个研究路线图，既描绘了应用于SE研究的DL技术的基础，并突出了未来可能的肥沃的探索领域。
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 article presents a systematic literature review of research at the intersection of SE & DL. The review canvasses work appearing in the most prominent SE and DL conferences and journals and spans 128 papers across 23 unique SE tasks. We center our analysis around the components of learning, a set of principles that governs 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 highlights likely areas of fertile exploration for the future.