research-article

Emergent, crowd-scale programming practice in the IDE

Authors:

Daniel Steffee,

Joel R. Brandt,

Michael S. BernsteinAuthors Info & Claims

CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages 2491 - 2500

https://doi.org/10.1145/2556288.2556998

Published: 26 April 2014 Publication History

Abstract

While emergent behaviors are uncodified across many domains such as programming and writing, interfaces need explicit rules to support users. We hypothesize that by codifying emergent programming behavior, software engineering interfaces can support a far broader set of developer needs. To explore this idea, we built Codex, a knowledge base that records common practice for the Ruby programming language by indexing over three million lines of popular code. Codex enables new data-driven interfaces for programming systems: statistical linting, identifying code that is unlikely to occur in practice and may constitute a bug; pattern annotation, automatically discovering common programming idioms and annotating them with metadata using expert crowdsourcing; and library generation, constructing a utility package that encapsulates and reflects emergent software practice. We evaluate these applications to find Codex captures a broad swatch of programming practice, statistical linting detects problematic code snippets, and pattern annotation discovers nontrivial idioms such as basic HTTP authentication and database migration templates. Our work suggests that operationalizing practice-driven knowledge in structured domains such as programming can enable a new class of user interfaces.

References

[1]

Ahmadzadeh, M., Elliman, D., and Higgins, C. An analysis of patterns of debugging among novice computer science students. In Proc. ITiCSE 2005.

Digital Library

[2]

Ayewah, N., et al. Using static analysis to find bugs. In IEEE Software 2008.

Digital Library

[3]

Baxter, I.D., et al. Clone detection using abstract syntax trees. In Proc. ICSM 1998.

Digital Library

[4]

Bernstein, M.S., et al. Direct answers for search queries in the long tail. In Proc. CHI 2012.

Digital Library

[5]

Brandt, J., Dontcheva, M., Weskamp, M., and Klemmer, S.R. Example-centric programming: integrating web search into the development environment. In Proc. CHI 2010.

Digital Library

[6]

Brandt, J., et al. Opportunistic programming: Writing code to prototype, ideate, and discover. In IEEE Software 2009.

Digital Library

[7]

Brandt, J., et al. Two studies of opportunistic programming: interleaving web foraging, learning, and writing code. In Proc. CHI 2009.

Digital Library

[8]

Buse, R.P.L. and Weimer, W. Synthesizing api usage examples. In Proc. ICSE 2012.

Digital Library

[9]

Ducasse, S., Rieger, M., and Demeyer, S. A language independent approach for detecting duplicated code. In Proc. ICSM 1999.

Digital Library

[10]

Engler, D., et al. Bugs as deviant behavior: a general approach to inferring errors in systems code. In Proc. SOSP 2001.

Digital Library

[11]

Fourney, A., Mann, R., and Terry, M. Query-feature graphs: bridging user vocabulary and system functionality. In Proc. UIST 2011.

Digital Library

[12]

Gabel, M. and Su, Z. A study of the uniqueness of source code. In Proc. FSE 2010.

Digital Library

[13]

Gamma, E., Helm, R., Johnson, R., and Vlissides, J. Design Patterns: Elements of Reusable Object-Oriented Software. 1994.

Digital Library

[14]

Goldman, M., Little, G., and Miller, R.C. Collabode: collaborative coding in the browser. In Proc. CHASE 2011.

Digital Library

[15]

Goldman, M. and Miller, R.C. Codetrail: Connecting source code and web resources. In Proc. VL/HCC 2009J.

Digital Library

[16]

Grechanik, M., et al. Exemplar: Executable examples archive. In Proc. ICSE 2010.

Digital Library

[17]

Greenberg, S. and Witten, I.H. How users repeat their actions on computers: Principles for design of history mechanisms. In Proc. CHI '88.

Digital Library

[18]

Hartmann, B., MacDougall, D., Brandt, J., and Klemmer, S.R. What would other programmers do: suggesting solutions to error messages. In Proc. of CHI 2010.

Digital Library

[19]

Hartmann, B., Wu, L., Collins, K., and Klemmer, S.R. Programming by a sample: rapidly creating web applications with d.mix. In Proc. UIST 2007.

Digital Library

[20]

Hindle, A., et al. On the naturalness of software. In Proc. ICSE 2012.

Digital Library

[21]

Holmes, R., Walker, R.J., and Murphy, G.C. Strathcona example recommendation tool. In Proc. FSE 2005.

Digital Library

[22]

Hummel, O., Janjic, W., and Atkinson, C. Code conjurer: Pulling reusable software out of thin air. IEEE Software 2008.

Digital Library

[23]

Kim, M., Bergman, L., Lau, T., and Notkin, D. An ethnographic study of copy and paste programming practices in oopl. In Proc. ISESE 2004.

Digital Library

[24]

Ko, A.J. and Myers, B.A. Designing the whyline: a debugging interface for asking questions about program behavior. In Proc. the CHI 2004.

Digital Library

[25]

Kumar, R., et al. Webzeitgeist: Design Mining the Web. In Proc. CHI 2013.

Digital Library

[26]

Mandelin, D., Xu, L., Bodík, R., and Kimelman, D. Jungloid mining: helping to navigate the api jungle. In Proc. PLDI 2005.

Digital Library

[27]

Matejka, J., Li, W., Grossman, T., and Fitzmaurice, G. CommunityCommands. In Proc. UIST 2009.

[28]

Mooty, M., Faulring, A., Stylos, J., and Myers, B.A. Calcite: Completing code completion for constructors using crowds. In Proc. VL/HCC 2010.

Digital Library

[29]

Sahavechaphan, N. and Claypool, K. Xsnippet: mining for sample code. In Proc. OOPSLA 2006.

Digital Library

[30]

Seacord, R.C., Plakosh, D., and Lewis, G.A. Modernizing Legacy Systems: Software Technologies, Engineering Process and Business Practices. 2003.

Digital Library

[31]

Simon, I., Morris, D., and Basu, S. MySong: automatic accompaniment generation for vocal melodies. In Proc. CHI 2008.

Digital Library

[32]

Stylos, J. and Myers, B.A. Mica: A web-search tool for finding api components and examples. In Proc. VL/HCC 2006.

Digital Library

[33]

Thummalapenta, S. and Xie, T. Parseweb: a programmer assistant for reusing open source code on the web. In Proc. ASE 2007.

Digital Library

[34]

Urma, R.G. and Mycroft, A. Programming language evolution via source code query languages. In Proc. PLATEAU 2012.

Digital Library

[35]

Ye, Y. and Fischer, G. Supporting reuse by delivering task-relevant and personalized information. In Proc. ICSE 2002.

Digital Library

Cited By

Ahmed ASalah AKhoriba GArafa T(2023)Localizing Non-functional Code Bugs in User Interfaces Using Deep Learning TechniquesModel and Data Engineering10.1007/978-3-031-49333-1_27(381-394)Online publication date: 22-Dec-2023
https://doi.org/10.1007/978-3-031-49333-1_27
Kotenko IIzrailov KBuinevich M(2022)Static Analysis of Information Systems for IoT Cyber Security: A Survey of Machine Learning ApproachesSensors10.3390/s2204133522:4(1335)Online publication date: 10-Feb-2022
https://doi.org/10.3390/s22041335
Soui MHaddad ZTrabelsi RSrinivasan K(2022)Deep features extraction to assess mobile user interfacesMultimedia Tools and Applications10.1007/s11042-022-11978-181:9(12945-12960)Online publication date: 22-Feb-2022
https://doi.org/10.1007/s11042-022-11978-1
Show More Cited By

Index Terms

Emergent, crowd-scale programming practice in the IDE
1. Human-centered computing
  1. Human computer interaction (HCI)

Recommendations

Elucidative programming

In this paper we introduce Elucidative Programming as a variant of Literate Programming. Literate Programming represents the idea of organizing a source program in an essay that documents the program understanding. An elucidative program connects ...
Gender differences and programming environments: across programming populations
ESEM '10: Proceedings of the 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement

Although there has been significant research into gender regarding educational and workplace practices, there has been little investigation of gender differences pertaining to problem solving with programming tools and environments. As a result, there ...
How novices tackle their first lines of code in an IDE: analysis of programming session traces
Koli Calling '14: Proceedings of the 14th Koli Calling International Conference on Computing Education Research

While computing educators have put plenty of effort into researching and developing programming environments that make it easier for students to create their first programs, these tools often have only little resemblance with the tools used in the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

April 2014

4206 pages

ISBN:9781450324731

DOI:10.1145/2556288

General Chairs:
Matt Jones
Swansea University, Wales, UK
,
Philippe Palanque
Université Paul Sabatier, France
,
Program Chairs:
Albrecht Schmidt
University of Stuttgart, Germany
,
Tovi Grossman
Autodesk Research, Canada

Copyright © 2014 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]

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 April 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CHI '14

Sponsor:

SIGCHI

CHI '14: CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2014

Ontario, Toronto, Canada

Acceptance Rates

CHI '14 Paper Acceptance Rate 465 of 2,043 submissions, 23%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

Upcoming Conference

CHI '25

Sponsor:
sigchi

CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2025

Yokohama , Japan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

26
Total Citations
View Citations
589
Total Downloads

Downloads (Last 12 months)18
Downloads (Last 6 weeks)2

Reflects downloads up to 09 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Ahmed ASalah AKhoriba GArafa T(2023)Localizing Non-functional Code Bugs in User Interfaces Using Deep Learning TechniquesModel and Data Engineering10.1007/978-3-031-49333-1_27(381-394)Online publication date: 22-Dec-2023
https://doi.org/10.1007/978-3-031-49333-1_27
Kotenko IIzrailov KBuinevich M(2022)Static Analysis of Information Systems for IoT Cyber Security: A Survey of Machine Learning ApproachesSensors10.3390/s2204133522:4(1335)Online publication date: 10-Feb-2022
https://doi.org/10.3390/s22041335
Soui MHaddad ZTrabelsi RSrinivasan K(2022)Deep features extraction to assess mobile user interfacesMultimedia Tools and Applications10.1007/s11042-022-11978-181:9(12945-12960)Online publication date: 22-Feb-2022
https://doi.org/10.1007/s11042-022-11978-1
Henley ABall JKlein BRutter ALee DErdogmus HMoreno A(2021)An inquisitive code editor for addressing novice programmers' misconceptions of program behaviorProceedings of the 43rd International Conference on Software Engineering: Joint Track on Software Engineering Education and Training10.1109/ICSE-SEET52601.2021.00026(165-170)Online publication date: 25-May-2021
https://dl.acm.org/doi/10.1109/ICSE-SEET52601.2021.00026
Schröder MCito J(2021)An empirical investigation of command-line customizationEmpirical Software Engineering10.1007/s10664-021-10036-y27:2Online publication date: 14-Dec-2021
https://doi.org/10.1007/s10664-021-10036-y
Krishna RGordon MFei-Fei LBernstein M(2021)Visual Intelligence through Human InteractionArtificial Intelligence for Human Computer Interaction: A Modern Approach10.1007/978-3-030-82681-9_9(257-314)Online publication date: 5-Nov-2021
https://doi.org/10.1007/978-3-030-82681-9_9
Zhao YFeng YWang YHao RFang CChen Z(2020)Quality assessment of crowdsourced test casesScience China Information Sciences10.1007/s11432-019-2859-863:9Online publication date: 10-Aug-2020
https://doi.org/10.1007/s11432-019-2859-8
Anderson CHübener ISeipp AOhly SDavid KPejovic V(2018)A Survey of Attention Management Systems in Ubiquitous Computing EnvironmentsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/32142612:2(1-27)Online publication date: 5-Jul-2018
https://dl.acm.org/doi/10.1145/3214261
Allamanis MBarr EDevanbu PSutton C(2018)A Survey of Machine Learning for Big Code and NaturalnessACM Computing Surveys10.1145/321269551:4(1-37)Online publication date: 31-Jul-2018
https://dl.acm.org/doi/10.1145/3212695
Ichinco MKelleher CGiannakos MJaccheri LDivitini M(2018)Semi-automatic suggestion generation for young novice programmers in an open-ended contextProceedings of the 17th ACM Conference on Interaction Design and Children10.1145/3202185.3202762(405-412)Online publication date: 19-Jun-2018
https://dl.acm.org/doi/10.1145/3202185.3202762
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents