A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research

2020/09/14 by Cody Watson, Nathan Cooper, Watson, Cody +8 · 1 voice · 10 citations
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Artificial intelligence #CLARITY #Computer science #Data science #Domain (mathematical analysis) #Engineering #FOS: Computer and information sciences #Intersection (aeronautics) #Machine Learning (cs.LG) #Management science #Neural and Evolutionary Computing (cs.NE) #Pace #Political science #Popularity #Set (abstract data type) #Software #Software Engineering (cs.SE) #Software Engineering Research #Software Engineering Techniques and Practices #Software System Performance and Reliability #Software engineering #Systematic review #Transport engineering #Work (physics) #cs.AI #cs.LG #cs.NE #cs.SE

paper · pdf · doi:10.48550/arxiv.2009.06520

published in arXiv (Cornell University) (Cornell University) · 59 pages, Accepted to TOSEM 2021

openalex publication_date 2020/09/14 · arxiv published 2020/09/14 · arxiv created 2021/09/23 · arxiv updated 2021/09/27 · openalex created_date 2022/07/25 · openalex updated_date 2026/08/06

Abstract

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 crosscutting 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 128 papers across 23 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 highlights likely areas of fertile exploration for the future.

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Discussions

• Literature Review on the Use of Deep Learning in Software Engineering Research [hn, 2 points, 0 comments]

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