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A search engine for finding highly relevant applications

Authors:

Mark Grechanik,

Collin McMillan,

Denys Poshyvanyk,

Chad CumbyAuthors Info & Claims

ICSE '10: Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1

Pages 475 - 484

https://doi.org/10.1145/1806799.1806868

Published: 01 May 2010 Publication History

Abstract

A fundamental problem of finding applications that are highly relevant to development tasks is the mismatch between the high-level intent reflected in the descriptions of these tasks and low-level implementation details of applications. To reduce this mismatch we created an approach called Exemplar (EXEcutable exaMPLes ARchive) for finding highly relevant software projects from large archives of applications. After a programmer enters a natural-language query that contains high-level concepts (e.g., MIME, data sets), Exemplar uses information retrieval and program analysis techniques to retrieve applications that implement these concepts. Our case study with 39 professional Java programmers shows that Exemplar is more effective than Sourceforge in helping programmers to quickly find highly relevant applications.

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Cited By

Huang SCai BYu YLuo J(2024)ExCS: accelerating code search with code expansionScientific Reports10.1038/s41598-024-73907-614:1Online publication date: 25-Nov-2024
https://doi.org/10.1038/s41598-024-73907-6
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

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Published In

cover image ACM Conferences

ICSE '10: Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1

May 2010

627 pages

ISBN:9781605587196

DOI:10.1145/1806799

General Chairs:
Jeff Kramer
Imperial College, London, UK
,
Judith Bishop
Microsoft Research, Redmond
,
Program Chairs:
Prem Devanbu
University of California at Davis
,
Sebastian Uchitel
University of Buenos Aires, Argentina and Imperial College London, UK

Copyright © 2010 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: 01 May 2010

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ICSE '10: 32nd International Conference on Software Engineering

May 1 - 8, 2010

Cape Town, South Africa

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Overall Acceptance Rate 276 of 1,856 submissions, 15%

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Cited By

Huang SCai BYu YLuo J(2024)ExCS: accelerating code search with code expansionScientific Reports10.1038/s41598-024-73907-614:1Online publication date: 25-Nov-2024
https://doi.org/10.1038/s41598-024-73907-6
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
Ma YYu YLi SJia ZMa JXu RDong WLiao X(2023)MulCS: Towards a Unified Deep Representation for Multilingual Code Search2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER56733.2023.00021(120-131)Online publication date: Mar-2023
https://doi.org/10.1109/SANER56733.2023.00021
Yu YHuang ZShen GLi WShao Y(2023)ASTSDL: predicting the functionality of incomplete programming code via an AST-sequence-based deep learning modelScience China Information Sciences10.1007/s11432-021-3665-167:1Online publication date: 27-Dec-2023
https://doi.org/10.1007/s11432-021-3665-1
Nguyen TYadavally ANguyen T(2022)Phrase2Set: Phrase-to-Set Machine Translation and Its Software Engineering Applications2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER53432.2022.00068(502-513)Online publication date: Mar-2022
https://doi.org/10.1109/SANER53432.2022.00068
Yu YHuang ZShen GLi WShao Y(2022)ASTENS-BWAScience of Computer Programming10.1016/j.scico.2022.102839222:COnline publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1016/j.scico.2022.102839
Chenaina TNeji SShoeb A(2021)Query Sense Discovery Approach to Realize the User's Search IntentInternational Journal of Information Retrieval Research10.4018/IJIRR.28960912:1(1-18)Online publication date: 15-Oct-2021
https://doi.org/10.4018/IJIRR.289609
ARIK AOKYAY SADAR N(2021)Hybrid Course Recommendation System Design for a Real-Time Student Automation ApplicationEuropean Journal of Science and Technology10.31590/ejosat.944596Online publication date: 23-Jun-2021
https://doi.org/10.31590/ejosat.944596
Liu CXia XLo DLiu ZHassan ALi S(2021)CodeMatcher: Searching Code Based on Sequential Semantics of Important Query WordsACM Transactions on Software Engineering and Methodology10.1145/346540331:1(1-37)Online publication date: 28-Sep-2021
https://dl.acm.org/doi/10.1145/3465403
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