research-article

Recommendation Systems for Software Engineering

Authors:

Martin Robillard,

Thomas ZimmermannAuthors Info & Claims

IEEE Software, Volume 27, Issue 4

Pages 80 - 86

https://doi.org/10.1109/MS.2009.161

Published: 01 July 2010 Publication History

Abstract

Software development can be challenging because of the large information spaces that developers must navigate. Without assistance, developers can become bogged down and spend a disproportionate amount of their time seeking information at the expense of other value-producing tasks. Recommendation systems for software engineering (RSSEs) are software tools that can assist developers with a wide range of activities, from reusing code to writing effective bug reports. The authors provide an overview of recommendation systems for software engineering: what they are, what they can do for developers, and what they might do in the future.

Cited By

Tufano RMastropaolo APepe FDabic ODi Penta MBavota GSpinellis DConstantinou EBacchelli A(2024)Unveiling ChatGPT's Usage in Open Source Projects: A Mining-based StudyProceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644918(571-583)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643991.3644918
Williams DCallan JKirbas SMechtaev SPetke JPrideaux-Ghee TSarro FRoychoudhury APaiva AAbreu RStorey MAniche MNagappan N(2024)User-Centric Deployment of Automated Program Repair at BloombergProceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice10.1145/3639477.3639756(81-91)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3639477.3639756
Almonte LGuerra ECantador Ide Lara J(2024)Engineering recommender systems for modelling languages: concept, tool and evaluationEmpirical Software Engineering10.1007/s10664-024-10483-329:4Online publication date: 18-Jun-2024
https://dl.acm.org/doi/10.1007/s10664-024-10483-3
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Index Terms

Recommendation Systems for Software Engineering
1. Software and its engineering
  1. Software creation and management
    1. Software development process management

Index terms have been assigned to the content through auto-classification.

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Published In

cover image IEEE Software

IEEE Software Volume 27, Issue 4

July 2010

93 pages

ISSN:0740-7459

Issue’s Table of Contents

Copyright © 2010.

Publisher

IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 July 2010

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Cited By

Tufano RMastropaolo APepe FDabic ODi Penta MBavota GSpinellis DConstantinou EBacchelli A(2024)Unveiling ChatGPT's Usage in Open Source Projects: A Mining-based StudyProceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644918(571-583)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643991.3644918
Williams DCallan JKirbas SMechtaev SPetke JPrideaux-Ghee TSarro FRoychoudhury APaiva AAbreu RStorey MAniche MNagappan N(2024)User-Centric Deployment of Automated Program Repair at BloombergProceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice10.1145/3639477.3639756(81-91)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3639477.3639756
Almonte LGuerra ECantador Ide Lara J(2024)Engineering recommender systems for modelling languages: concept, tool and evaluationEmpirical Software Engineering10.1007/s10664-024-10483-329:4Online publication date: 18-Jun-2024
https://dl.acm.org/doi/10.1007/s10664-024-10483-3
Mora Segura Áde Lara JWimmer M(2024)Modelling assistants based on information reuse: a user evaluation for language engineeringSoftware and Systems Modeling (SoSyM)10.1007/s10270-023-01094-523:1(57-84)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s10270-023-01094-5
Guo HGuo ZLiu Z(2023)Personalized Recommendation Model Based on Improved GRU Network in Big Data EnvironmentJournal of Electrical and Computer Engineering10.1155/2023/31622202023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/3162220
Menzen JOliveira K(2023)4Experts: A Task Recommendation Approach Using Machine Learning and Biometric DataProceedings of the XIX Brazilian Symposium on Information Systems10.1145/3592813.3592882(1-8)Online publication date: 29-May-2023
https://dl.acm.org/doi/10.1145/3592813.3592882
Belgacem HLi XBianculli DBriand L(2023)A Machine Learning Approach for Automated Filling of Categorical Fields in Data Entry FormsACM Transactions on Software Engineering and Methodology10.1145/353302132:2(1-40)Online publication date: 4-Apr-2023
https://dl.acm.org/doi/10.1145/3533021
Sas DAvgeriou P(2023)An Architectural Technical Debt Index Based on Machine Learning and Architectural SmellsIEEE Transactions on Software Engineering10.1109/TSE.2023.328617949:8(4169-4195)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TSE.2023.3286179
Sadi MYu E(2023)WEBAPIK: a body of structured knowledge on designing web APIsRequirements Engineering10.1007/s00766-023-00401-228:3(441-479)Online publication date: 14-Mar-2023
https://dl.acm.org/doi/10.1007/s00766-023-00401-2
Tan SLi ZYan L(2023)CrossFixJournal of Software: Evolution and Process10.1002/smr.255436:4Online publication date: 23-Mar-2023
https://dl.acm.org/doi/10.1002/smr.2554
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