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Taking Flight with Copilot: Early insights and opportunities of AI-powered pair-programming tools

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Idan GazitAuthors Info & Claims

Queue, Volume 20, Issue 6

Pages 35 - 57

https://doi.org/10.1145/3582083

Published: 26 January 2023 Publication History

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Abstract

Over the next five years, AI-powered tools likely will be helping developers in many diverse tasks. For example, such models may be used to improve code review, directing reviewers to parts of a change where review is most needed or even directly providing feedback on changes. Models such as Codex may suggest fixes for defects in code, build failures, or failing tests. These models are able to write tests automatically, helping to improve code quality and downstream reliability of distributed systems. This study of Copilot shows that developers spend more time reviewing code than actually writing code. As AI-powered tools are integrated into more software development tasks, developer roles will shift so that more time is spent assessing suggestions related to the task than doing the task itself.

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Cited By

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Mendes WSouza Sde Souza C(2024)Colaboração com Assistente de Codificação Baseado em IA: Benefícios e DesafiosAnais do XIX Simpósio Brasileiro de Sistemas Colaborativos (SBSC 2024)10.5753/sbsc.2024.237964(228-236)Online publication date: 29-Apr-2024
https://doi.org/10.5753/sbsc.2024.237964
Liu JLi S(2024)Toward Artificial Intelligence-Human Paired Programming: A Review of the Educational Applications and Research on Artificial Intelligence Code-Generation ToolsJournal of Educational Computing Research10.1177/07356331241240460Online publication date: 4-Apr-2024
https://doi.org/10.1177/07356331241240460
Chen T(2024)The Impact of AI-Pair Programmers on Code Quality and Developer Satisfaction: Evidence from TiMi studioProceedings of the 2024 International Conference on Generative Artificial Intelligence and Information Security10.1145/3665348.3665383(201-205)Online publication date: 10-May-2024
https://dl.acm.org/doi/10.1145/3665348.3665383
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Taking Flight with Copilot: Early insights and opportunities of AI-powered pair-programming tools

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Published In

Queue Volume 20, Issue 6

Copilot

November/December

84 pages

ISSN:1542-7730

EISSN:1542-7749

DOI:10.1145/3582502

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: 26 January 2023

Published in QUEUE Volume 20, Issue 6

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Mendes WSouza Sde Souza C(2024)Colaboração com Assistente de Codificação Baseado em IA: Benefícios e DesafiosAnais do XIX Simpósio Brasileiro de Sistemas Colaborativos (SBSC 2024)10.5753/sbsc.2024.237964(228-236)Online publication date: 29-Apr-2024
https://doi.org/10.5753/sbsc.2024.237964
Liu JLi S(2024)Toward Artificial Intelligence-Human Paired Programming: A Review of the Educational Applications and Research on Artificial Intelligence Code-Generation ToolsJournal of Educational Computing Research10.1177/07356331241240460Online publication date: 4-Apr-2024
https://doi.org/10.1177/07356331241240460
Chen T(2024)The Impact of AI-Pair Programmers on Code Quality and Developer Satisfaction: Evidence from TiMi studioProceedings of the 2024 International Conference on Generative Artificial Intelligence and Information Security10.1145/3665348.3665383(201-205)Online publication date: 10-May-2024
https://dl.acm.org/doi/10.1145/3665348.3665383
Dunay OCheng DTait AThakkar PRigby PChiu AAhmad IGanesan AMaddila CMurali VTayyebi ANagappan Nd'Amorim M(2024)Multi-line AI-Assisted Code AuthoringCompanion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering10.1145/3663529.3663836(150-160)Online publication date: 10-Jul-2024
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Weber TBrandmaier MSchmidt AMayer S(2024)Significant Productivity Gains through Programming with Large Language ModelsProceedings of the ACM on Human-Computer Interaction10.1145/36611458:EICS(1-29)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3661145
Kou BChen SWang ZMa LZhang T(2024)Do Large Language Models Pay Similar Attention Like Human Programmers When Generating Code?Proceedings of the ACM on Software Engineering10.1145/36608071:FSE(2261-2284)Online publication date: 12-Jul-2024
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Cheng RWang RZimmermann TFord D(2024)“It would work for me too”: How Online Communities Shape Software Developers’ Trust in AI-Powered Code Generation ToolsACM Transactions on Interactive Intelligent Systems10.1145/365199014:2(1-39)Online publication date: 15-May-2024
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de Oliveira BCastor FBaysal OLinares-Vasquez MMoran KSteinmacher I(2024)AthenaLLM: Supporting Experiments with Large Language Models in Software DevelopmentProceedings of the 32nd IEEE/ACM International Conference on Program Comprehension10.1145/3643916.3644437(69-73)Online publication date: 13-Jun-2024
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