research-article

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

Authors:

David Nader Palacio,

Denys PoshyvanykAuthors Info & Claims

ACM Transactions on Software Engineering and Methodology (TOSEM), Volume 31, Issue 2

Article No.: 32, Pages 1 - 58

https://doi.org/10.1145/3485275

Published: 04 March 2022 Publication History

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 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.

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https://doi.org/10.1016/j.infsof.2024.107588
Hou XZhao YLiu YYang ZWang KLi LLuo XLo DGrundy JWang H(2024)Large Language Models for Software Engineering: A Systematic Literature ReviewACM Transactions on Software Engineering and Methodology10.1145/3695988Online publication date: 20-Sep-2024
https://doi.org/10.1145/3695988
Zhao YGong LHuang ZWang YWei MWu FFilkov VRay BZhou M(2024)Coding-PTMs: How to Find Optimal Code Pre-trained Models for Code Embedding in Vulnerability Detection?Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695539(1732-1744)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695539
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Index Terms

A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research
1. Software and its engineering
  1. Software creation and management
    1. Software development techniques

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Published In

cover image ACM Transactions on Software Engineering and Methodology

ACM Transactions on Software Engineering and Methodology Volume 31, Issue 2

April 2022

789 pages

ISSN:1049-331X

EISSN:1557-7392

DOI:10.1145/3492439

Editor:
Mauro Pezzè
USI Università della Svizzera italiana and SIT Schaffhausen Institute of Technology, Switzerland

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 March 2022

Accepted: 01 September 2021

Revised: 01 July 2021

Received: 01 September 2020

Published in TOSEM Volume 31, Issue 2

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Cited By

Di Rocco JNguyen PDi Sipio CRubei RDi Ruscio DDi Penta M(2025)DeepMig: A transformer-based approach to support coupled library and code migrationsInformation and Software Technology10.1016/j.infsof.2024.107588177(107588)Online publication date: Jan-2025
https://doi.org/10.1016/j.infsof.2024.107588
Hou XZhao YLiu YYang ZWang KLi LLuo XLo DGrundy JWang H(2024)Large Language Models for Software Engineering: A Systematic Literature ReviewACM Transactions on Software Engineering and Methodology10.1145/3695988Online publication date: 20-Sep-2024
https://doi.org/10.1145/3695988
Zhao YGong LHuang ZWang YWei MWu FFilkov VRay BZhou M(2024)Coding-PTMs: How to Find Optimal Code Pre-trained Models for Code Embedding in Vulnerability Detection?Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695539(1732-1744)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695539
Chen JZhang YZhang LZhang MWan CSu TPu GFilkov VRay BZhou M(2024)FIPSER: Improving Fairness Testing of DNN by Seed PrioritizationProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695486(1069-1081)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695486
Dong LLu QZhu LMenzies TXu BKang HZhang J(2024)A Pilot Study in Surveying Data Challenges of Automatic Software Engineering TasksProceedings of the 4th International Workshop on Software Engineering and AI for Data Quality in Cyber-Physical Systems/Internet of Things10.1145/3663530.3665020(6-11)Online publication date: 15-Jul-2024
https://dl.acm.org/doi/10.1145/3663530.3665020
Yang HNong YZhang TLuo XCai H(2024)Learning to Detect and Localize Multilingual BugsProceedings of the ACM on Software Engineering10.1145/36608041:FSE(2190-2213)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3660804
Misu MLopes CMa INoble J(2024)Towards AI-Assisted Synthesis of Verified Dafny MethodsProceedings of the ACM on Software Engineering10.1145/36437631:FSE(812-835)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3643763
Xiao YSong WAhmed SGe XViswanath BMeng NYao D(2024)Measurement of Embedding Choices on Cryptographic API Completion TasksACM Transactions on Software Engineering and Methodology10.1145/362529133:3(1-30)Online publication date: 15-Mar-2024
https://dl.acm.org/doi/10.1145/3625291
Nader Palacio DVelasco ACooper NRodriguez AMoran KPoshyvanyk D(2024)Toward a Theory of Causation for Interpreting Neural Code ModelsIEEE Transactions on Software Engineering10.1109/TSE.2024.337994350:5(1215-1243)Online publication date: May-2024
https://doi.org/10.1109/TSE.2024.3379943
Wang JHuang YChen CLiu ZWang SWang Q(2024)Software Testing With Large Language Models: Survey, Landscape, and VisionIEEE Transactions on Software Engineering10.1109/TSE.2024.336820850:4(911-936)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1109/TSE.2024.3368208
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