Article

Method-call recommendations from implicit developer feedback

Authors:

Sven Amann,

Sebastian Proksch,

Mira MeziniAuthors Info & Claims

CSI-SE 2014: Proceedings of the 1st International Workshop on CrowdSourcing in Software Engineering

Pages 5 - 6

https://doi.org/10.1145/2593728.2593730

Published: 02 June 2014 Publication History

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Abstract

When developers use the code completion in their Integrated Development Environment (IDE), they provide implicit feedback about the usage of the Application Programming Interfaces (APIs) they program against. We demonstrate how to apply Collaborative Filtering techniques to compute context-sensitive completion recommendations from such feedback and discuss how the approach can be used to bring the knowledge of the crowd to every developer.

References

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S. Amann. Code Completion Based on Implicit User Feedback. Master’s thesis, Technische Universität Darmstadt, 2013.

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M. Bruch. IDE 2.0: Leveraging the Wisdom of the Software Engineering Crowds. PhD thesis, Technische Universität Darmstadt, 2012.

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R. Hill and J. Rideout. Automatic Method Completion. Proceedings of Conference on Automated Software Engineering (ASE), pages 228–235, 2004.

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B. Marlin. Collaborative Filtering: A Machine Learning Perspective. Master’s thesis, University of Toronto, 2004.

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Cited By

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Zhao JLiu Y(2019)APIUaaS: a reference architecture for facilitating API usage from a data analytics perspectiveIET Software10.1049/iet-sen.2018.535513:5(466-478)Online publication date: Oct-2019
https://doi.org/10.1049/iet-sen.2018.5355
Nguyen ANguyen TPhan HNguyen T(2018)A deep neural network language model with contexts for source code2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER.2018.8330220(323-334)Online publication date: Mar-2018
https://doi.org/10.1109/SANER.2018.8330220
Proksch SNadi SAmann SMezini M(2017)Enriching in-IDE process information with fine-grained source code history2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER.2017.7884626(250-260)Online publication date: Feb-2017
https://doi.org/10.1109/SANER.2017.7884626
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Index Terms

Method-call recommendations from implicit developer feedback
1. Software and its engineering
  1. Software notations and tools
    1. Development frameworks and environments

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Published In

CSI-SE 2014: Proceedings of the 1st International Workshop on CrowdSourcing in Software Engineering

June 2014

18 pages

ISBN:9781450328579

DOI:10.1145/2593728

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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TCSE: IEEE Computer Society's Tech. Council on Software Engin.

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Association for Computing Machinery

New York, NY, United States

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Published: 02 June 2014

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ICSE '14

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SIGSOFT

ICSE '14: 36th International Conference on Software Engineering

June 2, 2014

Hyderabad, India

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2025 IEEE/ACM 46th International Conference on Software Engineering

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Zhao JLiu Y(2019)APIUaaS: a reference architecture for facilitating API usage from a data analytics perspectiveIET Software10.1049/iet-sen.2018.535513:5(466-478)Online publication date: Oct-2019
https://doi.org/10.1049/iet-sen.2018.5355
Nguyen ANguyen TPhan HNguyen T(2018)A deep neural network language model with contexts for source code2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER.2018.8330220(323-334)Online publication date: Mar-2018
https://doi.org/10.1109/SANER.2018.8330220
Proksch SNadi SAmann SMezini M(2017)Enriching in-IDE process information with fine-grained source code history2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER.2017.7884626(250-260)Online publication date: Feb-2017
https://doi.org/10.1109/SANER.2017.7884626
Wu JShen LGuo WZhao W(2017)Code recommendation for android development: how does it work and what can be improved?Science China Information Sciences10.1007/s11432-017-9058-060:9Online publication date: 28-Jul-2017
https://doi.org/10.1007/s11432-017-9058-0
Wu JShen LGuo WZhao W(2016)How Is Code Recommendation Applied in Android Development: A Qualitative Review2016 International Conference on Software Analysis, Testing and Evolution (SATE)10.1109/SATE.2016.12(30-35)Online publication date: Nov-2016
https://doi.org/10.1109/SATE.2016.12
Proksch SBauer VMurphy G(2016)How to Build a Recommendation System for Software EngineeringSoftware Engineering10.1007/978-3-319-28406-4_1(1-42)Online publication date: 13-Jan-2016
https://doi.org/10.1007/978-3-319-28406-4_1

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Capturing and Exploiting IDE Interactions

Activeperl Developer's Guide

JBuilder Developer's Guide

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