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

TL;DR

This systematic review analyzes 128 papers on deep learning applications in software engineering, highlighting current trends, challenges, and future research directions in this rapidly evolving interdisciplinary field.

Contribution

It provides a comprehensive overview of DL techniques in SE, categorizes existing research, and offers a research roadmap for future exploration.

Findings

Deep learning is increasingly used for automating SE tasks.

Most research focuses on feature engineering and modeling software artifacts.

Future opportunities include addressing data scarcity and explainability.

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.