research-article

SnipMatch: using source code context to enhance snippet retrieval and parameterization

Authors:

Roel VertegaalAuthors Info & Claims

UIST '12: Proceedings of the 25th annual ACM symposium on User interface software and technology

Pages 219 - 228

https://doi.org/10.1145/2380116.2380145

Published: 07 October 2012 Publication History

Abstract

Programmers routinely use source code snippets to increase their productivity. However, locating and adapting code snippets to the current context still takes time: for example, variables must be renamed, and dependencies included. We believe that when programmers decide to invest time in creating a new code snippet from scratch, they would also be willing to spend additional effort to make that code snippet configurable and easy to integrate. To explore this insight, we built SnipMatch, a plug-in for the Eclipse IDE. SnipMatch introduces a simple markup that allows snippet authors to specify search patterns and integration instructions. SnipMatch leverages this information, in conjunction with current code context, to improve snippet search and parameterization. For example, when a search query includes local variables, SnipMatch suggests compatible snippets, and automatically adapts them by substituting in these variables. In the lab, we observed that participants integrated snippets faster when using SnipMatch than when using standard Eclipse. Findings from a public deployment to 93 programmers suggest that SnipMatch has become integrated into the work practices of real users.

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Cited By

Zhang TLu YYu YMao XZhang YZhao Y(2024)How Do Developers Adapt Code Snippets to Their Contexts? An Empirical Study of Context-Based Code Snippet AdaptationsIEEE Transactions on Software Engineering10.1109/TSE.2024.339551950:11(2712-2731)Online publication date: Nov-2024
https://doi.org/10.1109/TSE.2024.3395519
Kim KGhatpande SKim DZhou XLiu KBissyandé TKlein JLe Traon Y(2023)Big Code Search: A BibliographyACM Computing Surveys10.1145/360490556:1(1-49)Online publication date: 26-Aug-2023
https://dl.acm.org/doi/10.1145/3604905
Di Grazia LPradel M(2023)Code Search: A Survey of Techniques for Finding CodeACM Computing Surveys10.1145/356597155:11(1-31)Online publication date: 9-Feb-2023
https://dl.acm.org/doi/10.1145/3565971
Show More Cited By

Index Terms

SnipMatch: using source code context to enhance snippet retrieval and parameterization
1. Human-centered computing
  1. Human computer interaction (HCI)

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cover image ACM Conferences

UIST '12: Proceedings of the 25th annual ACM symposium on User interface software and technology

October 2012

608 pages

ISBN:9781450315807

DOI:10.1145/2380116

General Chair:
Rob Miller
MIT CSAIL, USA
,
Program Chairs:
Hrvoje Benko
Microsoft Research, USA
,
Celine Latulipe
University of North Carolina at Charlotte, USA

Copyright © 2012 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: 07 October 2012

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UIST '12

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UIST '12: The 25th Annual ACM Symposium on User Interface Software and Technology

October 7 - 10, 2012

Massachusetts, Cambridge, USA

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Overall Acceptance Rate 561 of 2,567 submissions, 22%

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Cited By

Zhang TLu YYu YMao XZhang YZhao Y(2024)How Do Developers Adapt Code Snippets to Their Contexts? An Empirical Study of Context-Based Code Snippet AdaptationsIEEE Transactions on Software Engineering10.1109/TSE.2024.339551950:11(2712-2731)Online publication date: Nov-2024
https://doi.org/10.1109/TSE.2024.3395519
Kim KGhatpande SKim DZhou XLiu KBissyandé TKlein JLe Traon Y(2023)Big Code Search: A BibliographyACM Computing Surveys10.1145/360490556:1(1-49)Online publication date: 26-Aug-2023
https://dl.acm.org/doi/10.1145/3604905
Di Grazia LPradel M(2023)Code Search: A Survey of Techniques for Finding CodeACM Computing Surveys10.1145/356597155:11(1-31)Online publication date: 9-Feb-2023
https://dl.acm.org/doi/10.1145/3565971
Storer KSampath HMerrick MKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)”It’s Just Everything Outside of the IDE that’s the Problem”:Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445090(1-12)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445090
Diamantopoulos TOikonomou NSymeonidis A(2020)Extracting Semantics from Question-Answering Services for Snippet ReuseFundamental Approaches to Software Engineering10.1007/978-3-030-45234-6_6(119-139)Online publication date: 17-Apr-2020
https://doi.org/10.1007/978-3-030-45234-6_6
Diamantopoulos TSymeonidis ADiamantopoulos TSymeonidis A(2020)Mining Source Code for Snippet ReuseMining Software Engineering Data for Software Reuse10.1007/978-3-030-30106-4_7(175-192)Online publication date: 31-Mar-2020
https://doi.org/10.1007/978-3-030-30106-4_7
Diamantopoulos TSymeonidis ADiamantopoulos TSymeonidis A(2020)Mining Source Code for Component ReuseMining Software Engineering Data for Software Reuse10.1007/978-3-030-30106-4_6(133-174)Online publication date: 31-Mar-2020
https://doi.org/10.1007/978-3-030-30106-4_6
Diamantopoulos TSymeonidis ADiamantopoulos TSymeonidis A(2020)Theoretical Background and State-of-the-ArtMining Software Engineering Data for Software Reuse10.1007/978-3-030-30106-4_2(13-43)Online publication date: 31-Mar-2020
https://doi.org/10.1007/978-3-030-30106-4_2
Liu MHsieh JHahn NZhou ADeng EBurley STaylor CKittur AMyers BGuimbretière FBernstein MReinecke K(2019)Unakite: Scaffolding Developers' Decision-Making Using the WebProceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology10.1145/3332165.3347908(67-80)Online publication date: 17-Oct-2019
https://dl.acm.org/doi/10.1145/3332165.3347908
Kelleher CIchinco M(2019)Towards a Model of API Learning2019 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)10.1109/VLHCC.2019.8818850(163-168)Online publication date: Oct-2019
https://doi.org/10.1109/VLHCC.2019.8818850
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