research-article

Open access

Grounded Copilot: How Programmers Interact with Code-Generating Models

Authors:

Shraddha Barke,

Michael B. James,

Nadia PolikarpovaAuthors Info & Claims

Proceedings of the ACM on Programming Languages, Volume 7, Issue OOPSLA1

Article No.: 78, Pages 85 - 111

https://doi.org/10.1145/3586030

Published: 06 April 2023 Publication History

Abstract

Powered by recent advances in code-generating models, AI assistants like Github Copilot promise to change the face of programming forever. But what is this new face of programming? We present the first grounded theory analysis of how programmers interact with Copilot, based on observing 20 participants—with a range of prior experience using the assistant—as they solve diverse programming tasks across four languages. Our main finding is that interactions with programming assistants are bimodal: in acceleration mode, the programmer knows what to do next and uses Copilot to get there faster; in exploration mode, the programmer is unsure how to proceed and uses Copilot to explore their options. Based on our theory, we provide recommendations for improving the usability of future AI programming assistants.

References

[1]

Matej Balog, Alexander L Gaunt, Marc Brockschmidt, Sebastian Nowozin, and Daniel Tarlow. 2016. Deepcoder: Learning to write programs. arXiv preprint arXiv:1611.01989.

[2]

Shraddha Barke, Michael B. James, and Nadia Polikarpova. 2023. Grounded Copilot: How Programmers Interact with Code-Generating Models. https://doi.org/10.5281/zenodo.7713789

Digital Library

[3]

Mohammad Bavarian, Heewoo Jun, Nikolas Tezak, John Schulman, Christine McLeavey, Jerry Tworek, and Mark Chen. 2022. Efficient Training of Language Models to Fill in the Middle. Jul, https://doi.org/10.48550/arXiv.2207.14255 arXiv:2207.14255 [cs].

[4]

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, and Amanda Askell. 2020. Language models are few-shot learners. Advances in neural information processing systems, 33 (2020), 1877–1901.

[5]

Antony Bryant and Kathy Charmaz. 2007. The SAGE Handbook of Grounded Theory. SAGE Publications Ltd. isbn:978-1-4129-2346-0 https://doi.org/10.4135/9781848607941

[6]

Donal E Carlston. 2013. Dual-Process Theories. isbn:978-0-19-998468-8 http://public.ebookcentral.proquest.com/choice/publicfullrecord.aspx?p=1336453

[7]

Jeff Carver. 2004. The Impact of Background and Experience on Software Inspections. Empirical Softw. Engg., 9, 3 (2004), sep, 259–262. issn:1382-3256 https://doi.org/10.1023/B:EMSE.0000027786.04555.97

Digital Library

[8]

Sarah E. Chasins, Maria Mueller, and Rastislav Bodik. 2018. Rousillon: Scraping Distributed Hierarchical Web Data. In Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology (UIST ’18). Association for Computing Machinery, New York, NY, USA. 963–975. isbn:9781450359481 https://doi.org/10.1145/3242587.3242661

Digital Library

[9]

Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, and Greg Brockman. 2021. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374.

[10]

Koen Claessen and John Hughes. 2000. QuickCheck: a lightweight tool for random testing of Haskell programs. In Proceedings of the fifth ACM SIGPLAN international conference on Functional programming (ICFP’00). Association for Computing Machinery, 268–279. isbn:978-1-58113-202-1 https://doi.org/10.1145/351240.351266

Digital Library

[11]

Michael Coblenz, Gauri Kambhatla, Paulette Koronkevich, Jenna L. Wise, Celeste Barnaby, Joshua Sunshine, Jonathan Aldrich, and Brad A. Myers. 2021. PLIERS: A Process that Integrates User-Centered Methods into Programming Language Design. ACM Transactions on Computer-Human Interaction, 28, 4 (2021), Jul, 28:1–28:53. issn:1073-0516 https://doi.org/10.1145/3452379

Digital Library

[12]

Mihaly Csikszentmihalyi. 2014. Flow and the Foundations of Positive Psychology. Springer Netherlands. isbn:978-94-017-9087-1 https://doi.org/10.1007/978-94-017-9088-8

[13]

Françoise Détienne and Frank Bott. 2001. Software design—cognitive aspects. Springer-Verlag. isbn:978-1-85233-253-2

[14]

Ian Drosos, Titus Barik, Philip J. Guo, Robert DeLine, and Sumit Gulwani. 2020. Wrex: A Unified Programming-by-Example Interaction for Synthesizing Readable Code for Data Scientists. Association for Computing Machinery, New York, NY, USA. 1–12. isbn:9781450367080 https://doi.org/10.1145/3313831.3376442

Digital Library

[15]

Kasra Ferdowsifard, Shraddha Barke, Hila Peleg, Sorin Lerner, and Nadia Polikarpova. 2021. LooPy: Interactive Program Synthesis with Control Structures. Proc. ACM Program. Lang., 5, OOPSLA (2021), Article 153, oct, 29 pages. https://doi.org/10.1145/3485530

Digital Library

[16]

Kasra Ferdowsifard, Allen Ordookhanians, Hila Peleg, Sorin Lerner, and Nadia Polikarpova. 2020. Small-Step Live Programming by Example. In Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology. Association for Computing Machinery, New York, NY, USA. 614–626. isbn:9781450375146 https://doi.org/10.1145/3379337.3415869

Digital Library

[17]

Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer, and Mike Lewis. 2022. InCoder: A Generative Model for Code Infilling and Synthesis. Apr, https://doi.org/10.48550/arXiv.2204.05999 arXiv:2204.05999 [cs].

[18]

Nat Friedman. 2021. https://github.blog/2021-06-29-introducing-github-copilot-ai-pair-programmer/

[19]

Barney G. Glaser and Anselm L. Strauss. 1967. The discovery of grounded theory: strategies for qualitative research (5. paperback print ed.). Aldine Transaction. isbn:978-0-202-30260-7

[20]

Elena L. Glassman, Jeremy Scott, Rishabh Singh, Philip J. Guo, and Robert C. Miller. 2015. OverCode: Visualizing Variation in Student Solutions to Programming Problems at Scale. ACM Trans. Comput.-Hum. Interact., 22, 2 (2015), Article 7, mar, 35 pages. issn:1073-0516 https://doi.org/10.1145/2699751

Digital Library

[21]

Sumit Gulwani. 2011. Automating string processing in spreadsheets using input-output examples. ACM Sigplan Notices, 46, 1 (2011), 317–330.

Digital Library

[22]

Daya Guo, Alexey Svyatkovskiy, Jian Yin, Nan Duan, Marc Brockschmidt, and Miltiadis Allamanis. 2021. Learning to Complete Code with Sketches. In International Conference on Learning Representations.

[23]

Michael B. James, Zheng Guo, Ziteng Wang, Shivani Doshi, Hila Peleg, Ranjit Jhala, and Nadia Polikarpova. 2020. Digging for Fold: Synthesis-Aided API Discovery for Haskell. Proc. ACM Program. Lang., 4, OOPSLA (2020), Article 205, nov, 27 pages. https://doi.org/10.1145/3428273

Digital Library

[24]

Dhanya Jayagopal, Justin Lubin, and Sarah E Chasins. 2022. Exploring the Learnability of Program Synthesizers by Novice Programmers. 15.

[25]

Ellen Jiang, Edwin Toh, Alejandra Molina, Kristen Olson, Claire Kayacik, Aaron Donsbach, Carrie J Cai, and Michael Terry. 2022. Discovering the Syntax and Strategies of Natural Language Programming with Generative Language Models. In CHI Conference on Human Factors in Computing Systems. 1–19.

[26]

Daniel Kahneman. 2011. Thinking, fast and slow. Penguin Books. isbn:978-0-14-103357-0

[27]

Ashwin Kalyan, Abhishek Mohta, Oleksandr Polozov, Dhruv Batra, Prateek Jain, and Sumit Gulwani. 2018. Neural-guided deductive search for real-time program synthesis from examples. arXiv preprint arXiv:1804.01186.

[28]

Kite. 2020. Kite: AI-Powered Completions for JupyterLab. https://www.kite.com/integrations/jupyter/

[29]

Matthew Lee. 2020. Detecting Affective Flow States of Knowledge Workers Using Physiological Sensors. arXiv:2006.10635 [cs], Jun, arxiv:2006.10635 arXiv: 2006.10635.

[30]

Sorin Lerner. 2020. Projection Boxes: On-the-fly Reconfigurable Visualization for Live Programming. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. ACM, 1–7. isbn:978-1-4503-6708-0 https://doi.org/10.1145/3313831.3376494

Digital Library

[31]

Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, Thomas Hubert, Peter Choy, Cyprien de Masson d’Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Alexey Cherepanov, James Molloy, Daniel J. Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu, and Oriol Vinyals. 2022. Competition-Level Code Generation with AlphaCode. https://doi.org/10.48550/ARXIV.2203.07814

[32]

Justin Lubin and Sarah E. Chasins. 2021. How statically-typed functional programmers write code. Proceedings of the ACM on Programming Languages, 5, OOPSLA (2021), Oct, 1–30. issn:2475-1421 https://doi.org/10.1145/3485532

Digital Library

[33]

Smitha Milli, Falk Lieder, and Thomas L. Griffiths. 2021. A rational reinterpretation of dual-process theories. Cognition, 217 (2021), 104881. issn:0010-0277 https://doi.org/10.1016/j.cognition.2021.104881

[34]

Anders Miltner, Sumit Gulwani, Vu Le, Alan Leung, Arjun Radhakrishna, Gustavo Soares, Ashish Tiwari, and Abhishek Udupa. 2019. On the fly synthesis of edit suggestions. Proceedings of the ACM on Programming Languages, 3, OOPSLA (2019), 1–29.

Digital Library

[35]

Michael Muller. 2014. Curiosity, Creativity, and Surprise as Analytic Tools: Grounded Theory Method. Springer, 25–48. isbn:978-1-4939-0378-8 https://doi.org/10.1007/978-1-4939-0378-8_2

[36]

Brad A. Myers, Amy J. Ko, Thomas D. LaToza, and YoungSeok Yoon. 2016. Programmers Are Users Too: Human-Centered Methods for Improving Programming Tools. Computer, 49, 7 (2016), Jul, 44–52. issn:0018-9162 https://doi.org/10.1109/MC.2016.200

Digital Library

[37]

Wode Ni, Joshua Sunshine, Vu Le, Sumit Gulwani, and Titus Barik. 2021. reCode: A Lightweight Find-and-Replace Interaction in the IDE for Transforming Code by Example. In The 34th Annual ACM Symposium on User Interface Software and Technology. 258–269.

Digital Library

[38]

Cyrus Omar, Ian Voysey, Ravi Chugh, and Matthew A. Hammer. 2019. Live Functional Programming with Typed Holes. Proc. ACM Program. Lang., 3, POPL (2019), Jan, 14:1–14:32. issn:2475-1421 https://doi.org/10.1145/3290327

Digital Library

[39]

Hammond Pearce, Baleegh Ahmad, Benjamin Tan, Brendan Dolan-Gavitt, and Ramesh Karri. 2021. Asleep at the Keyboard? Assessing the Security of GitHub Copilot’s Code Contributions. arXiv preprint arXiv:2108.09293.

[40]

Hila Peleg, Roi Gabay, Shachar Itzhaky, and Eran Yahav. 2020. Programming with a Read-Eval-Synth Loop. Proc. ACM Program. Lang., 4, OOPSLA (2020), Article 159, nov, 30 pages. https://doi.org/10.1145/3428227

Digital Library

[41]

Hila Peleg, Sharon Shoham, and Eran Yahav. 2018. Programming Not Only by Example. In Proceedings of the 40th International Conference on Software Engineering (ICSE ’18). ACM, 1114–1124. isbn:978-1-4503-5638-1 https://doi.org/10.1145/3180155.3180189 tex.ids: pelegProgrammingNotOnly2018a event-place: Gothenburg, Sweden.

Digital Library

[42]

Nancy Pennington. 1987. Stimulus structures and mental representations in expert comprehension of computer programs. Cognitive Psychology, 19, 3 (1987), Jul, 295–341. issn:00100285 https://doi.org/10.1016/0010-0285(87)90007-7

[43]

Veselin Raychev, Martin Vechev, and Eran Yahav. 2014. Code completion with statistical language models. In Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation. 419–428.

Digital Library

[44]

Advait Sarkar, Andrew D Gordon, Carina Negreanu, Christian Poelitz, Sruti Srinivasa Ragavan, and Ben Zorn. 2022. What is it like to program with artificial intelligence? arXiv preprint arXiv:2208.06213.

[45]

Armando Solar-Lezama. 2013. Program sketching. International Journal on Software Tools for Technology Transfer, 15, 5–6 (2013), Oct, 475–495. issn:1433-2779, 1433-2787 https://doi.org/10.1007/s10009-012-0249-7

Digital Library

[46]

Klaas-Jan Stol, Paul Ralph, and Brian Fitzgerald. 2016. Grounded theory in software engineering research: a critical review and guidelines. In Proceedings of the 38th International Conference on Software Engineering (ICSE ’16). Association for Computing Machinery, 120–131. isbn:978-1-4503-3900-1 https://doi.org/10.1145/2884781.2884833

Digital Library

[47]

Anselm L. Strauss and Juliet Corbin. 1990. Basics of Qualitative Reseach: Grounded Theory Procedures and Techniques. SAGE Publications, Inc. isbn:0-8039-3250-2

[48]

TabNine. 2018. TabNine: AI Assistant for Development Teams. https://www.tabnine.com/

[49]

Steven J. Taylor and Robert Bogdan. 1998. Introduction to qualitative research methods: A guidebook and resource, 3rd ed. John Wiley and Sons Inc. isbn:978-0-471-16868-3

[50]

Priyan Vaithilingam, Tianyi Zhang, and Elena Glassman. 2022. Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models. In CHI Late-Breaking Work.

[51]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention Is All You Need. Dec, https://doi.org/10.48550/arXiv.1706.03762 arXiv:1706.03762 [cs].

[52]

Regina Vollmeyer and Falko Rheinberg. 2006. Motivational Effects on Self-Regulated Learning with Different Tasks. Educational Psychology Review, 18, 3 (2006), Nov, 239–253. issn:1040-726X, 1573-336X https://doi.org/10.1007/s10648-006-9017-0

[53]

Chenglong Wang, Yu Feng, Rastislav Bodik, Isil Dillig, Alvin Cheung, and Amy J Ko. 2021. Falx: Synthesis-Powered Visualization Authoring. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI ’21). Association for Computing Machinery, New York, NY, USA. Article 106, 15 pages. isbn:9781450380966 https://doi.org/10.1145/3411764.3445249

Digital Library

[54]

Eric Wastl. 2021. Advent of Code. https://adventofcode.com/2021

[55]

Justin D. Weisz, Michael Muller, Stephanie Houde, John Richards, Steven I. Ross, Fernando Martinez, Mayank Agarwal, and Kartik Talamadupula. 2021. Perfection Not Required? Human-AI Partnerships in Code Translation. In 26th International Conference on Intelligent User Interfaces. Association for Computing Machinery, New York, NY, USA. 402–412. isbn:9781450380171 https://doi.org/10.1145/3397481.3450656

Digital Library

[56]

Frank F. Xu, Zhengbao Jiang, Pengcheng Yin, Bogdan Vasilescu, and Graham Neubig. 2020. Incorporating External Knowledge through Pre-training for Natural Language to Code Generation. https://doi.org/10.48550/ARXIV.2004.09015

[57]

Frank F. Xu, Bogdan Vasilescu, and Graham Neubig. 2021. In-IDE Code Generation from Natural Language: Promise and Challenges. https://doi.org/10.48550/ARXIV.2101.11149

[58]

Wojciech Zaremba, Greg Brockman, and OpenAI. 2021. Codex. https://openai.com/blog/openai-codex/

[59]

Tianyi Zhang, Zhiyang Chen, Yuanli Zhu, Priyan Vaithilingam, Xinyu Wang, and Elena L. Glassman. 2021. Interpretable Program Synthesis. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI ’21). Association for Computing Machinery, New York, NY, USA. Article 105, 16 pages. isbn:9781450380966 https://doi.org/10.1145/3411764.3445646

Digital Library

[60]

Tianyi Zhang, London Lowmanstone, Xinyu Wang, and Elena L. Glassman. 2020. Interactive Program Synthesis by Augmented Examples. In Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology. Association for Computing Machinery, New York, NY, USA. 627–648. isbn:9781450375146 https://doi.org/10.1145/3379337.3415900

Digital Library

[61]

Xiangyu Zhou, Ras Bodik, Alvin Cheung, and Chenglong Wang. 2022. Synthesizing Analytical SQL Queries from Computation Demonstration. In PLDI.

Cited By

Mendes WSouza Sde Souza C(2024)Colaboração com Assistente de Codificação Baseado em IA: Benefícios e DesafiosAnais do XIX Simpósio Brasileiro de Sistemas Colaborativos (SBSC 2024)10.5753/sbsc.2024.237964(228-236)Online publication date: 29-Apr-2024
https://doi.org/10.5753/sbsc.2024.237964
Poitras ECrane BDempsey DBragg TSiegel ALin M(2024)Cognitive Apprenticeship and Artificial Intelligence Coding AssistantsNavigating Computer Science Education in the 21st Century10.4018/979-8-3693-1066-3.ch013(261-281)Online publication date: 26-Feb-2024
https://doi.org/10.4018/979-8-3693-1066-3.ch013
Ashtari NChilana P(2024)How New Developers Approach Augmented Reality Development Using Simplified Creation Tools: An Observational StudyMultimodal Technologies and Interaction10.3390/mti80400358:4(35)Online publication date: 22-Apr-2024
https://doi.org/10.3390/mti8040035
Show More Cited By

Index Terms

Grounded Copilot: How Programmers Interact with Code-Generating Models
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI theory, concepts and models
2. Software and its engineering
  1. Software creation and management

Recommendations

How Statically-Typed Functional Programmers Author Code
CHI EA '21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems

How working statically-typed functional programmers author code is largely understudied. And yet, a better understanding of developer practices could pave the way for the design of more useful and usable tooling, more ergonomic languages, and more ...
Choose your programming copilot: a comparison of the program synthesis performance of github copilot and genetic programming
GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference

GitHub Copilot, an extension for the Visual Studio Code development environment powered by the large-scale language model Codex, makes automatic program synthesis available for software developers. This model has been extensively studied in the field of ...
Beginning COBOL for Programmers

Comments

Information & Contributors

Information

Published In

cover image Proceedings of the ACM on Programming Languages

Proceedings of the ACM on Programming Languages Volume 7, Issue OOPSLA1

April 2023

901 pages

EISSN:2475-1421

DOI:10.1145/3554309

Editor:
Michael Hicks
Amazon, USA

Issue’s Table of Contents

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 April 2023

Published in PACMPL Volume 7, Issue OOPSLA1

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Badges

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

55
Total Citations
View Citations
4,832
Total Downloads

Downloads (Last 12 months)3,741
Downloads (Last 6 weeks)353

Reflects downloads up to 11 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Mendes WSouza Sde Souza C(2024)Colaboração com Assistente de Codificação Baseado em IA: Benefícios e DesafiosAnais do XIX Simpósio Brasileiro de Sistemas Colaborativos (SBSC 2024)10.5753/sbsc.2024.237964(228-236)Online publication date: 29-Apr-2024
https://doi.org/10.5753/sbsc.2024.237964
Poitras ECrane BDempsey DBragg TSiegel ALin M(2024)Cognitive Apprenticeship and Artificial Intelligence Coding AssistantsNavigating Computer Science Education in the 21st Century10.4018/979-8-3693-1066-3.ch013(261-281)Online publication date: 26-Feb-2024
https://doi.org/10.4018/979-8-3693-1066-3.ch013
Ashtari NChilana P(2024)How New Developers Approach Augmented Reality Development Using Simplified Creation Tools: An Observational StudyMultimodal Technologies and Interaction10.3390/mti80400358:4(35)Online publication date: 22-Apr-2024
https://doi.org/10.3390/mti8040035
Song FAgarwal AWen W(2024)The Impact of Generative AI on Collaborative Open-Source Software Development: Evidence from GitHub CopilotSSRN Electronic Journal10.2139/ssrn.4856935Online publication date: 2024
https://doi.org/10.2139/ssrn.4856935
Liu JLi S(2024)Toward Artificial Intelligence-Human Paired Programming: A Review of the Educational Applications and Research on Artificial Intelligence Code-Generation ToolsJournal of Educational Computing Research10.1177/07356331241240460Online publication date: 4-Apr-2024
https://doi.org/10.1177/07356331241240460
Khemka MHouck B(2024)Toward Effective AI Support for DevelopersQueue10.1145/367541622:3(53-78)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3675416
de Moor Avan Deursen AIzadi MAdams BZimmermann TOzkaya ILin DZhang J(2024)A Transformer-Based Approach for Smart Invocation of Automatic Code CompletionProceedings of the 1st ACM International Conference on AI-Powered Software10.1145/3664646.3664760(28-37)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3664646.3664760
Brown AD'Angelo SMurillo AJaspan CGreen CAdams BZimmermann TOzkaya ILin DZhang J(2024)Identifying the Factors That Influence Trust in AI Code CompletionProceedings of the 1st ACM International Conference on AI-Powered Software10.1145/3664646.3664757(1-9)Online publication date: 10-Jul-2024
https://doi.org/10.1145/3664646.3664757
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
https://dl.acm.org/doi/10.1145/3663529.3663836
Khojah RMohamad MLeitner Pde Oliveira Neto F(2024)Beyond Code Generation: An Observational Study of ChatGPT Usage in Software Engineering PracticeProceedings of the ACM on Software Engineering10.1145/36607881:FSE(1819-1840)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3660788
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

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 Article

Media

Figures

Other

Tables

View Issue’s Table of Contents