research-article

Learning from examples to improve code completion systems

Authors:

Martin Monperrus,

Mira MeziniAuthors Info & Claims

ESEC/FSE '09: Proceedings of the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering

Pages 213 - 222

https://doi.org/10.1145/1595696.1595728

Published: 24 August 2009 Publication History

Abstract

The suggestions made by current IDE's code completion features are based exclusively on static type system of the programming language. As a result, often proposals are made which are irrelevant for a particular working context. Also, these suggestions are ordered alphabetically rather than by their relevance in a particular context. In this paper, we present intelligent code completion systems that learn from existing code repositories. We have implemented three such systems, each using the information contained in repositories in a different way. We perform a large-scale quantitative evaluation of these systems, integrate the best performing one into Eclipse, and evaluate the latter also by a user study. Our experiments give evidence that intelligent code completion systems which learn from examples significantly outperform mainstream code completion systems in terms of the relevance of their suggestions and thus have the potential to enhance developers' productivity.

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Cited By

Liu YYin YDeng JLi WPeng Z(2024)A Combinatorial Strategy for API Completion: Deep Learning and HeuristicsElectronics10.3390/electronics1318366913:18(3669)Online publication date: 15-Sep-2024
https://doi.org/10.3390/electronics13183669
Kamran PDevanbu PStanford CFilkov VRay BZhou M(2024)Vision Paper: Proof-Carrying Code CompletionsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering Workshops10.1145/3691621.3694932(35-42)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691621.3694932
Yu XLi CPan MLi XFilkov VRay BZhou M(2024)DroidCoder: Enhanced Android Code Completion with Context-Enriched Retrieval-Augmented GenerationProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695063(681-693)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695063
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Index Terms

Learning from examples to improve code completion systems
1. Software and its engineering
  1. Software notations and tools
    1. Development frameworks and environments
      1. Integrated and visual development environments

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Published In

cover image ACM Conferences

ESEC/FSE '09: Proceedings of the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering

August 2009

408 pages

ISBN:9781605580012

DOI:10.1145/1595696

General Chair:
Hans van Vliet
VU University Amsterdam, The Netherlands
,
Program Chair:
Valerie Issarny
INRIA-Paris-Rocquencourt, France

Copyright © 2009 ACM.

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 ACM 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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Published: 24 August 2009

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ESEC/FSE09

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ESEC/FSE09: Joint 12th European Software Engineering Conference

August 24 - 28, 2009

Amsterdam, The Netherlands

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ESEC/FSE '09 Paper Acceptance Rate 32 of 217 submissions, 15%;

Overall Acceptance Rate 112 of 543 submissions, 21%

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Cited By

Liu YYin YDeng JLi WPeng Z(2024)A Combinatorial Strategy for API Completion: Deep Learning and HeuristicsElectronics10.3390/electronics1318366913:18(3669)Online publication date: 15-Sep-2024
https://doi.org/10.3390/electronics13183669
Kamran PDevanbu PStanford CFilkov VRay BZhou M(2024)Vision Paper: Proof-Carrying Code CompletionsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering Workshops10.1145/3691621.3694932(35-42)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691621.3694932
Yu XLi CPan MLi XFilkov VRay BZhou M(2024)DroidCoder: Enhanced Android Code Completion with Context-Enriched Retrieval-Augmented GenerationProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695063(681-693)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695063
Razzaq ABuckley JLai QYu TBotterweck G(2024)A Systematic Literature Review on the Influence of Enhanced Developer Experience on Developers' Productivity: Factors, Practices, and RecommendationsACM Computing Surveys10.1145/368729957:1(1-46)Online publication date: 7-Oct-2024
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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
Zhu TLiu ZXu TTang ZZhang TPan MXia XBaysal OLinares-Vasquez MMoran KSteinmacher I(2024)Exploring and Improving Code Completion for Test CodeProceedings of the 32nd IEEE/ACM International Conference on Program Comprehension10.1145/3643916.3644421(137-148)Online publication date: 15-Apr-2024
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Corso VMariani LMicucci DRiganelli OBaysal OLinares-Vasquez MMoran KSteinmacher I(2024)Generating Java Methods: An Empirical Assessment of Four AI-Based Code AssistantsProceedings of the 32nd IEEE/ACM International Conference on Program Comprehension10.1145/3643916.3644402(13-23)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643916.3644402
Murali VMaddila CAhmad IBolin MCheng DGhorbani NFernandez RNagappan NRigby P(2024)AI-Assisted Code Authoring at Scale: Fine-Tuning, Deploying, and Mixed Methods EvaluationProceedings of the ACM on Software Engineering10.1145/36437741:FSE(1066-1085)Online publication date: 12-Jul-2024
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Velasco APalacio DRodriguez-Cardenas DPoshyvanyk DRoychoudhury APaiva AAbreu RStorey MHierons RMadeira H(2024)Which Syntactic Capabilities Are Statistically Learned by Masked Language Models for Code?Proceedings of the 2024 ACM/IEEE 44th International Conference on Software Engineering: New Ideas and Emerging Results10.1145/3639476.3639768(72-76)Online publication date: 14-Apr-2024
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