research-article

Will Code Remain a Relevant User Interface for End-User Programming with Generative AI Models?

Author:

Advait SarkarAuthors Info & Claims

Onward! 2023: Proceedings of the 2023 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software

Pages 153 - 167

https://doi.org/10.1145/3622758.3622882

Published: 19 October 2023 Publication History

Abstract

The research field of end-user programming has largely been concerned with helping non-experts learn to code sufficiently well in order to achieve their tasks. Generative AI stands to obviate this entirely by allowing users to generate code from naturalistic language prompts. In this essay, we explore the extent to which "traditional" programming languages remain relevant for non-expert end-user programmers in a world with generative AI. We posit the "generative shift hypothesis": that generative AI will create qualitative and quantitative expansions in the traditional scope of end-user programming. We outline some reasons that traditional programming languages may still be relevant and useful for end-user programmers. We speculate whether each of these reasons might be fundamental and enduring, or whether they may disappear with further improvements and innovations in generative AI. Finally, we articulate a set of implications for end-user programming research, including the possibility of needing to revisit many well-established core concepts, such as Ko's learning barriers and Blackwell's attention investment model.

References

[1]

Saeed Aghaee, Alan F Blackwell, David Stillwell, and Michal Kosinski. 2015. Personality and intrinsic motivational factors in end-user programming. In 2015 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). 29–36.

[2]

Blake Alcott. 2005. Jevons’ paradox. Ecological economics, 54, 1 (2005), 9–21.

[3]

Ian Arawjo. 2020. To write code: The cultural fabrication of programming notation and practice. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–15.

Digital Library

[4]

Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, and Quoc Le. 2021. Program synthesis with large language models. arXiv preprint arXiv:2108.07732.

[5]

Antranig Basman, Luke Church, Clemens Nylandsted Klokmose, and Colin BD Clark. 2016. Software and How it Lives On-Embedding Live Programs in the World Around Them. In PPIG. 19.

[6]

Brett A Becker, Paul Denny, James Finnie-Ansley, Andrew Luxton-Reilly, James Prather, and Eddie Antonio Santos. 2023. Programming is hard-or at least it used to be: Educational opportunities and challenges of ai code generation. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1. 500–506.

Digital Library

[7]

Laura Beckwith, Cory Kissinger, Margaret Burnett, Susan Wiedenbeck, Joseph Lawrance, Alan Blackwell, and Curtis Cook. 2006. Tinkering and gender in end-user programmers’ debugging. In Proceedings of the SIGCHI conference on Human Factors in computing systems. 231–240.

Digital Library

[8]

Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency. 610–623.

Digital Library

[9]

Alan Blackwell. 2022. Chapter 13: Conclusion. MIT Press. https://moralcodes.pubpub.org/pub/chapter-13

[10]

Alan F Blackwell. 2002. First steps in programming: A rationale for attention investment models. In Proceedings IEEE 2002 Symposia on Human Centric Computing Languages and Environments. 2–10.

[11]

Alan F Blackwell. 2002. What is programming? In PPIG. 20.

[12]

Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, and Emma Brunskill. 2021. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.

[13]

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.

[14]

Margaret Burnett, Simone Stumpf, Jamie Macbeth, Stephann Makri, Laura Beckwith, Irwin Kwan, Anicia Peters, and William Jernigan. 2016. GenderMag: A method for evaluating software’s gender inclusiveness. Interacting with Computers, 28, 6 (2016), 760–787.

[15]

Li Cai. 2012. Latent variable modeling. Shanghai Arch. Psychiatry, 24, 2 (2012), April, 118–120.

[16]

George Chalhoub and Advait Sarkar. 2022. “It’s Freedom to Put Things Where My Mind Wants”: Understanding and Improving the User Experience of Structuring Data in Spreadsheets. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. 1–24.

Digital Library

[17]

Nick Collins, Alex McLean, Julian Rohrhuber, and Adrian Ward. 2003. Live coding in laptop performance. Organised sound, 8, 3 (2003), 321–330.

[18]

David Coyle, James Moore, Per Ola Kristensson, Paul Fletcher, and Alan Blackwell. 2012. I did that! Measuring users’ experience of agency in their own actions. In Proceedings of the SIGCHI conference on human factors in computing systems. 2025–2034.

Digital Library

[19]

Allen Cypher. 1991. Eager: Programming repetitive tasks by example. In Proceedings of the SIGCHI conference on Human factors in computing systems. 33–39.

Digital Library

[20]

Allen Cypher and Daniel Conrad Halbert. 1993. Watch what I do: programming by demonstration. MIT press.

Digital Library

[21]

Peter J Denning. 2017. Remaining trouble spots with computational thinking. Commun. ACM, 60, 6 (2017), 33–39.

Digital Library

[22]

Paul Denny, Viraj Kumar, and Nasser Giacaman. 2023. Conversing with copilot: Exploring prompt engineering for solving cs1 problems using natural language. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1. 1136–1142.

Digital Library

[23]

Paul Denny, James Prather, Brett A Becker, James Finnie-Ansley, Arto Hellas, Juho Leinonen, Andrew Luxton-Reilly, Brent N Reeves, Eddie Antonio Santos, and Sami Sarsa. 2023. Computing Education in the Era of Generative AI. arXiv preprint arXiv:2306.02608.

[24]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.

[25]

Umberto Eco. 1997. The search for the perfect language. Blackwell, London, England.

[26]

B. S. Everitt. 1984. An Introduction to Latent Variable Models (1 ed.). Springer Dordrecht. isbn:978-94-010-8954-8 https://doi.org/10.1007/978-94-009-5564-6

[27]

Zhiyu Fan, Xiang Gao, Abhik Roychoudhury, and Shin Hwei Tan. 2022. Improving automatically generated code from Codex via Automated Program Repair. arXiv preprint arXiv:2205.10583.

[28]

Sally Fincher and Marian Petre. 2004. Computer science education research. CRC Press.

Digital Library

[29]

Michel Foucault. 1966. Les mots et les choses: une archéologie des sciences humaines. Editions Gallimard.

[30]

Maria I Gorinova, Advait Sarkar, Alan F Blackwell, and Don Syme. 2016. A live, multiple-representation probabilistic programming environment for novices. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. 2533–2537.

Digital Library

[31]

Thomas R. G. Green and Marian Petre. 1996. Usability analysis of visual programming environments: a ‘cognitive dimensions’ framework. Journal of Visual Languages & Computing, 7, 2 (1996), 131–174.

[32]

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

Digital Library

[33]

Andrei Hagiu and Julia Wright. 2020. A Practical Guide to Building Ethical AI. https://hbr.org/2020/10/a-practical-guide-to-building-ethical-ai

[34]

Stefan Harrer. 2023. Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine. EBioMedicine, 90 (2023).

[35]

Felienne Hermans, Martin Pinzger, and Arie Van Deursen. 2011. Supporting professional spreadsheet users by generating leveled dataflow diagrams. In Proceedings of the 33rd International Conference on Software Engineering. 451–460.

Digital Library

[36]

Felienne Hermans, Martin Pinzger, and Arie van Deursen. 2015. Detecting and refactoring code smells in spreadsheet formulas. Empirical Software Engineering, 20 (2015), 549–575.

Digital Library

[37]

JON M. JACHIMOWICZ, SHANNON DUNCAN, ELKE U. WEBER, and ERIC J. JOHNSON. 2019. When and why defaults influence decisions: a meta-analysis of default effects. Behavioural Public Policy, 3, 2 (2019), 159–186. https://doi.org/10.1017/bpp.2018.43

[38]

Gonzalo Jaimovitch-López, Cèsar Ferri, José Hernández-Orallo, Fernando Martínez-Plumed, and María José Ramírez-Quintana. 2022. Can language models automate data wrangling? Machine Learning, 1–30.

[39]

Krish Jain and Steven L Tanimoto. 2020. Integrating a Live Programming Role into Games. In PPIG.

[40]

Joel Jakubovic, Jonathan Edwards, and Tomas Petricek. 2023. Technical Dimensions of Programming Systems. arXiv preprint arXiv:2302.10003.

[41]

Will Jernigan, Amber Horvath, Michael Lee, Margaret Burnett, Taylor Cuilty, Sandeep Kuttal, Anicia Peters, Irwin Kwan, Faezeh Bahmani, and Amy Ko. 2015. A principled evaluation for a principled Idea Garden. In 2015 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). 235–243.

[42]

Shuyang Jiang, Yuhao Wang, and Yu Wang. 2023. SelfEvolve: A Code Evolution Framework via Large Language Models. arXiv preprint arXiv:2306.02907.

[43]

Nima Joharizadeh, Advait Sarkar, Andrew D Gordon, and Jack Williams. 2020. Gridlets: Reusing spreadsheet grids. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems. 1–7.

Digital Library

[44]

Sungmin Kang, Bei Chen, Shin Yoo, and Jian-Guang Lou. 2023. Explainable Automated Debugging via Large Language Model-driven Scientific Debugging. arXiv preprint arXiv:2304.02195.

[45]

Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020. Scaling laws for neural language models. arXiv preprint arXiv:2001.08361.

[46]

Daniel Martin Katz, Michael James Bommarito, Shang Gao, and Pablo Arredondo. 2023. Gpt-4 passes the bar exam. Available at SSRN 4389233.

[47]

Henry Kautz. 2022. The third AI summer: AAAI Robert S. Engelmore memorial lecture. AI Magazine, 43, 1 (2022), 105–125.

Digital Library

[48]

Mary Beth Kery and Brad A Myers. 2017. Exploring exploratory programming. In 2017 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). 25–29.

[49]

Jay P Kesan and Rajiv C Shah. 2006. Setting software defaults: Perspectives from law, computer science and behavioral economics. Notre Dame L. Rev., 82 (2006), 583.

[50]

Anirudh Khatry, Joyce Cahoon, Jordan Henkel, Shaleen Deep, Venkatesh Emani, Avrilia Floratou, Sumit Gulwani, Vu Le, Mohammad Raza, and Sherry Shi. 2023. From Words to Code: Harnessing Data for Program Synthesis from Natural Language. arXiv preprint arXiv:2305.01598.

[51]

Aleksandra Klašnja-Milićević, Boban Vesin, Mirjana Ivanović, and Zoran Budimac. 2011. Integration of recommendations and adaptive hypermedia into Java tutoring system. Computer Science and Information Systems, 8, 1 (2011), 211–224.

[52]

Clemens N Klokmose, James R Eagan, Siemen Baader, Wendy Mackay, and Michel Beaudouin-Lafon. 2015. Webstrates: shareable dynamic media. In Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology. 280–290.

Digital Library

[53]

Amy J Ko, Robin Abraham, Laura Beckwith, Alan Blackwell, Margaret Burnett, Martin Erwig, Chris Scaffidi, Joseph Lawrance, Henry Lieberman, and Brad Myers. 2011. The state of the art in end-user software engineering. ACM Computing Surveys (CSUR), 43, 3 (2011), 1–44.

Digital Library

[54]

Amy J Ko and Brad A Myers. 2004. Designing the whyline: a debugging interface for asking questions about program behavior. In Proceedings of the SIGCHI conference on Human factors in computing systems. 151–158.

Digital Library

[55]

Amy J Ko, Brad A Myers, and Htet Htet Aung. 2004. Six learning barriers in end-user programming systems. In 2004 IEEE Symposium on Visual Languages-Human Centric Computing. 199–206.

Digital Library

[56]

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2017. Imagenet classification with deep convolutional neural networks. Commun. ACM, 60, 6 (2017), 84–90.

Digital Library

[57]

Todd Kulesza, Margaret Burnett, Weng-Keen Wong, and Simone Stumpf. 2015. Principles of explanatory debugging to personalize interactive machine learning. In Proceedings of the 20th international conference on intelligent user interfaces. 126–137.

Digital Library

[58]

Julia A Lasserre, Christopher M Bishop, and Thomas P Minka. 2006. Principled hybrids of generative and discriminative models. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06). 1, 87–94.

Digital Library

[59]

Sam Lau, Sruti Srinivasa Srinivasa Ragavan, Ken Milne, Titus Barik, and Advait Sarkar. 2021. Tweakit: Supporting end-user programmers who transmogrify code. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1–12.

Digital Library

[60]

Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. nature, 521, 7553 (2015), 436–444.

[61]

Ben Liblit, Andrew Begel, and Eve Sweetser. 2006. Cognitive Perspectives on the Role of Naming in Computer Programs. In PPIG. 11.

[62]

Henry Lieberman. 2001. Your wish is my command: Programming by example. Morgan Kaufmann.

[63]

Hannah Limerick, David Coyle, and James W. Moore. 2014. The experience of agency in human-computer interactions: a review. Frontiers in Human Neuroscience, 8 (2014), issn:1662-5161 https://doi.org/10.3389/fnhum.2014.00643

[64]

Michael Xieyang Liu, Advait Sarkar, Carina Negreanu, Benjamin Zorn, Jack Williams, Neil Toronto, and Andrew D. Gordon. 2023. “What It Wants Me To Say”: Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). Association for Computing Machinery, New York, NY, USA. Article 598, 31 pages. isbn:9781450394215 https://doi.org/10.1145/3544548.3580817

Digital Library

[65]

Vivian Liu and Lydia B Chilton. 2022. Design guidelines for prompt engineering text-to-image generative models. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. 1–23.

Digital Library

[66]

Wendy E Mackay. 1991. Triggers and barriers to customizing software. In Proceedings of the SIGCHI conference on Human factors in computing systems. 153–160.

Digital Library

[67]

Adrian Mackenzie. 2017. Machine learners: Archaeology of a data practice. MIT Press.

[68]

Stephen MacNeil, Andrew Tran, Arto Hellas, Joanne Kim, Sami Sarsa, Paul Denny, Seth Bernstein, and Juho Leinonen. 2023. Experiences from using code explanations generated by large language models in a web software development e-book. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1. 931–937.

Digital Library

[69]

Stephen MacNeil, Andrew Tran, Juho Leinonen, Paul Denny, Joanne Kim, Arto Hellas, Seth Bernstein, and Sami Sarsa. 2022. Automatically Generating CS Learning Materials with Large Language Models. arXiv preprint arXiv:2212.05113.

[70]

Zohar Manna and Richard J Waldinger. 1971. Toward automatic program synthesis. Commun. ACM, 14, 3 (1971), 151–165.

Digital Library

[71]

David Metz. 2021. Economic benefits of road widening: Discrepancy between outturn and forecast. Transportation research part A: policy and practice, 147 (2021), 312–319.

[72]

Donna J Middaugh. 2018. Delusion of Control: Pushing Buttons. Medsurg Nursing, 27, 6 (2018), 399.

[73]

Tim Miller. 2019. Explanation in artificial intelligence: Insights from the social sciences. Artificial intelligence, 267 (2019), 1–38.

[74]

Swaroop Mishra, Daniel Khashabi, Chitta Baral, Yejin Choi, and Hannaneh Hajishirzi. 2021. Reframing Instructional Prompts to GPTk’s Language. arXiv preprint arXiv:2109.07830.

[75]

Anders Mørch. 1997. Three levels of end-user tailoring: Customization, integration, and extension. Computers and design in context, 1997 (1997), 61.

[76]

Stephan Morgenthaler. 2009. Exploratory data analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 1, 1 (2009), 33–44.

Digital Library

[77]

Midas Nouwens and Clemens Nylandsted Klokmose. 2018. The application and its consequences for non-standard knowledge work. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1–12.

Digital Library

[78]

Arika Okrent. 2009. In the land of invented languages: Esperanto rock stars, Klingon poets, Loglan lovers, and the mad dreamers who tried to build a perfect language. Random House.

[79]

OpenAI. 2023. GPT-4 Technical Report. arxiv:2303.08774.

[80]

Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, and Alex Ray. 2022. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35 (2022), 27730–27744.

[81]

Rahul Pandita, Chris Parnin, Felienne Hermans, and Emerson Murphy-Hill. 2018. No half-measures: A study of manual and tool-assisted end-user programming tasks in Excel. In 2018 ieee symposium on visual languages and human-centric computing (vl/hcc). 95–103.

[82]

Tomas Petricek. 2016. Programming language theory: Thinking the unthinkable (Work in progress). In Proceedings of the Annual Conference of the Psychology of Programming Interest Group (PPIG).

[83]

Tomas Petricek. 2022. No-code, no thought? substrates for simple programming for all. https://tomasp.net/blog/2022/no-code-substrates/

[84]

Martin Potthast, Matthias Hagen, and Benno Stein. 2021. The dilemma of the direct answer. In Acm sigir forum. 54, 1–12.

[85]

Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021. Zero-shot text-to-image generation. In International Conference on Machine Learning. 8821–8831.

[86]

Mitchel Resnick, John Maloney, Andrés Monroy-Hernández, Natalie Rusk, Evelyn Eastmond, Karen Brennan, Amon Millner, Eric Rosenbaum, Jay Silver, and Brian Silverman. 2009. Scratch: programming for all. Commun. ACM, 52, 11 (2009), 60–67.

Digital Library

[87]

Michael Revow, Christopher KI Williams, and Geoffrey E Hinton. 1996. Using generative models for handwritten digit recognition. IEEE transactions on pattern analysis and machine intelligence, 18, 6 (1996), 592–606.

[88]

Anna Rogers, Olga Kovaleva, and Anna Rumshisky. 2021. A primer in BERTology: What we know about how BERT works. Transactions of the Association for Computational Linguistics, 8 (2021), 842–866.

[89]

Advait Sarkar. 2022. Is explainable AI a race against model complexity? In Workshop on Transparency and Explanations in Smart Systems (TeXSS), in conjunction with ACM Intelligent User Interfaces (IUI 2022) (CEUR Workshop Proceedings, 3124). 192–199. http://ceur-ws.org/Vol-3124/paper22.pdf

[90]

Advait Sarkar. 2023. Exploring Perspectives on the Impact of Artificial Intelligence on the Creativity of Knowledge Work: Beyond Mechanised Plagiarism and Stochastic Parrots. In Annual Symposium on Human-Computer Interaction for Work 2023 (CHIWORK 2023). ACM, Oldenburg, Germany. 17. https://doi.org/10.1145/3596671.3597650

Digital Library

[91]

Advait Sarkar. 2023. Should Computers Be Easy To Use? Questioning the Doctrine of Simplicity in User Interface Design. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (CHI EA ’23). Association for Computing Machinery, New York, NY, USA. Article 419, 10 pages. isbn:9781450394222 https://doi.org/10.1145/3544549.3582741

Digital Library

[92]

Advait Sarkar and Andrew D. Gordon. 2018. How do people learn to use spreadsheets? (Work in progress). In Proceedings of the 29th Annual Conference of the Psychology of Programming Interest Group (PPIG 2018). 28–35.

[93]

Advait Sarkar and Andrew D Gordon. 2018. How do people learn to use spreadsheets?(Work in progress). In PPIG.

[94]

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? In Proceedings of the 33rd Annual Conference of the Psychology of Programming Interest Group (PPIG 2022).

[95]

Advait Sarkar, Sruti Srinivasa Ragavan, Jack Williams, and Andrew D Gordon. 2022. End-user encounters with lambda abstraction in spreadsheets: Apollo’s bow or Achilles’ heel? In 2022 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). 1–11.

[96]

Advait Sarkar, Martin Spott, Alan F Blackwell, and Mateja Jamnik. 2016. Visual discovery and model-driven explanation of time series patterns. In 2016 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). 78–86.

[97]

Christopher Scaffidi, Mary Shaw, and Brad Myers. 2005. Estimating the numbers of end users and end user programmers. In 2005 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC’05). 207–214.

Digital Library

[98]

Floarea Serban, Joaquin Vanschoren, Jörg-Uwe Kietz, and Abraham Bernstein. 2013. A survey of intelligent assistants for data analysis. ACM Computing Surveys (CSUR), 45, 3 (2013), 1–35.

Digital Library

[99]

Jaime Sevilla, Lennart Heim, Anson Ho, Tamay Besiroglu, Marius Hobbhahn, and Pablo Villalobos. 2022. Compute trends across three eras of machine learning. In 2022 International Joint Conference on Neural Networks (IJCNN). 1–8.

[100]

David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, and Marc Lanctot. 2016. Mastering the game of Go with deep neural networks and tree search. nature, 529, 7587 (2016), 484–489.

[101]

Nikhil Singh, Guillermo Bernal, Daria Savchenko, and Elena L Glassman. 2022. Where to hide a stolen elephant: Leaps in creative writing with multimodal machine intelligence. ACM Transactions on Computer-Human Interaction.

[102]

Irene Solaiman, Miles Brundage, Jack Clark, Amanda Askell, Ariel Herbert-Voss, Jeff Wu, Alec Radford, Gretchen Krueger, Jong Wook Kim, and Sarah Kreps. 2019. Release strategies and the social impacts of language models. arXiv preprint arXiv:1908.09203.

[103]

Sruti Srinivasa Ragavan, Sandeep Kaur Kuttal, Charles Hill, Anita Sarma, David Piorkowski, and Margaret Burnett. 2016. Foraging among an overabundance of similar variants. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. 3509–3521.

Digital Library

[104]

Sruti Srinivasa Ragavan, Advait Sarkar, and Andrew D Gordon. 2021. Spreadsheet comprehension: Guesswork, giving up and going back to the author. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1–21.

Digital Library

[105]

Alistair Stead and Alan F Blackwell. 2014. Learning syntax as notational expertise when using drawbridge. In Proceedings of the Psychology of Programming Interest Group Annual Conference (PPIG 2014). 41–52.

[106]

Hendrik Strobelt, Albert Webson, Victor Sanh, Benjamin Hoover, Johanna Beyer, Hanspeter Pfister, and Alexander M Rush. 2022. Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models. IEEE transactions on visualization and computer graphics, 29, 1 (2022), 1146–1156.

[107]

Tamara Sumner and Markus Stolze. 1997. Evolution, not revolution: Participatory design in the toolbelt era. MIT Press Cambridge, MA.

[108]

Bruno Tafur and Advait Sarkar. 2023. User Perceptions of Automatic Fake News Detection: Can Algorithms Fight Online Misinformation? arxiv:2304.07926.

[109]

Alex Tamkin, Miles Brundage, Jack Clark, and Deep Ganguli. 2021. Understanding the capabilities, limitations, and societal impact of large language models. arXiv preprint arXiv:2102.02503.

[110]

Steven L Tanimoto. 2013. A perspective on the evolution of live programming. In 2013 1st International Workshop on Live Programming (LIVE). 31–34.

[111]

Steven L Tanimoto. 2020. Multiagent live programming systems: Models and prospects for critical applications. In Companion Proceedings of the 4th International Conference on Art, Science, and Engineering of Programming. 90–96.

Digital Library

[112]

Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, and Faisal Azhar. 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.

[113]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, 30 (2017).

[114]

Pablo Villalobos, Jaime Sevilla, Lennart Heim, Tamay Besiroglu, Marius Hobbhahn, and Anson Ho. 2022. Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning. arXiv preprint arXiv:2211.04325.

[115]

David Vos, Till Döhmen, and Sebastian Schelter. 2022. Towards Parameter-Efficient Automation of Data Wrangling Tasks with Prefix-Tuning. In NeurIPS 2022 First Table Representation Workshop.

[116]

Yue Wang, Weishi Wang, Shafiq Joty, and Steven CH Hoi. 2021. Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation. arXiv preprint arXiv:2109.00859.

[117]

Laura Weidinger, John Mellor, Maribeth Rauh, Conor Griffin, Jonathan Uesato, Po-Sen Huang, Myra Cheng, Mia Glaese, Borja Balle, and Atoosa Kasirzadeh. 2021. Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359.

[118]

Jules White, Quchen Fu, Sam Hays, Michael Sandborn, Carlos Olea, Henry Gilbert, Ashraf Elnashar, Jesse Spencer-Smith, and Douglas C Schmidt. 2023. A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382.

[119]

Susan Wiedenbeck, Deborah Labelle, and Vennila NR Kain. 2004. Factors affecting course outcomes in introductory programming. In PPIG. 11.

[120]

Aaron Wilson, Margaret Burnett, Laura Beckwith, Orion Granatir, Ledah Casburn, Curtis Cook, Mike Durham, and Gregg Rothermel. 2003. Harnessing curiosity to increase correctness in end-user programming. In Proceedings of the SIGCHI conference on Human factors in computing systems. 305–312.

Digital Library

[121]

Jeannette M Wing. 2006. Computational thinking. Commun. ACM, 49, 3 (2006), 33–35.

Digital Library

[122]

Nuyun Zhang, Gang Huang, Ying Zhang, Ning Jiang, and Hong Mei. 2010. Towards Automated Synthesis of Executable Eclipse Tutorials. In SEKE. 591–598.

Cited By

Falconer J(2024)Knowledge Management at a Precipice: The Precarious State of Ideas, Facts, and Truth in the AI RenaissanceSSRN Electronic Journal10.2139/ssrn.4754610Online publication date: 2024
https://doi.org/10.2139/ssrn.4754610
Thiede CTaeumel MBöhme LHirschfeld REdwards JTaeumel M(2024)Talking to Objects in Natural Language: Toward Semantic Tools for Exploratory ProgrammingProceedings of the 2024 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software10.1145/3689492.3690049(68-84)Online publication date: 17-Oct-2024
https://dl.acm.org/doi/10.1145/3689492.3690049
Park GPanda PTankelevitch LRintel S(2024)The CoExplorer Technology Probe: A Generative AI-Powered Adaptive Interface to Support Intentionality in Planning and Running Video MeetingsProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661507(1638-1657)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661507
Show More Cited By

Index Terms

Will Code Remain a Relevant User Interface for End-User Programming with Generative AI Models?

Recommendations

End user programming/informal programming
End-user programming and blended-user programming
CHI EA '99: CHI '99 Extended Abstracts on Human Factors in Computing Systems

End-User Programming has not lived up to expectations: today's computer world is dominated by "fatware" programs with hundreds of features, not simple applications built by the users themselves. Yet a strange convergence is taking place between the ...
A Survey on End-User Robot Programming
As robots interact with a broader range of end-users, end-user robot programming has helped democratize robot programming by empowering end-users who may not have experience in robot programming to customize robots to meet their individual contextual ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

Onward! 2023: Proceedings of the 2023 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software

October 2023

204 pages

ISBN:9798400703881

DOI:10.1145/3622758

Program Chairs:
Tijs van der Storm
CWI, Netherlands / University of Groningen, Netherlands
,
Robert Hirschfeld
University of Potsdam, Germany / HPI, Germany

Copyright © 2023 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 the author(s) 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

SIGPLAN: ACM Special Interest Group on Programming Languages

In-Cooperation

SIGAda: ACM Special Interest Group on Ada Programming Language

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 October 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

Onward! '23

Sponsor:

SIGPLAN

Onward! '23: 2023 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software

October 25 - 27, 2023

Cascais, Portugal

Acceptance Rates

Overall Acceptance Rate 40 of 105 submissions, 38%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
572
Total Downloads

Downloads (Last 12 months)358
Downloads (Last 6 weeks)23

Reflects downloads up to 20 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Falconer J(2024)Knowledge Management at a Precipice: The Precarious State of Ideas, Facts, and Truth in the AI RenaissanceSSRN Electronic Journal10.2139/ssrn.4754610Online publication date: 2024
https://doi.org/10.2139/ssrn.4754610
Thiede CTaeumel MBöhme LHirschfeld REdwards JTaeumel M(2024)Talking to Objects in Natural Language: Toward Semantic Tools for Exploratory ProgrammingProceedings of the 2024 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software10.1145/3689492.3690049(68-84)Online publication date: 17-Oct-2024
https://dl.acm.org/doi/10.1145/3689492.3690049
Park GPanda PTankelevitch LRintel S(2024)The CoExplorer Technology Probe: A Generative AI-Powered Adaptive Interface to Support Intentionality in Planning and Running Video MeetingsProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661507(1638-1657)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661507
Denny PLeinonen JPrather JLuxton-Reilly AAmarouche TBecker BReeves BStephenson BStone JBattestilli LRebelsky SShoop L(2024)Prompt Problems: A New Programming Exercise for the Generative AI EraProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 110.1145/3626252.3630909(296-302)Online publication date: 7-Mar-2024
https://dl.acm.org/doi/10.1145/3626252.3630909
Falconer J(undefined)Unnatural: Artificial Selection as Flawed Metaphor for Organizational ChangeSSRN Electronic Journal10.2139/ssrn.3836974
https://doi.org/10.2139/ssrn.3836974
Robinson DCabrera CGordon ALawrence NMennen L(undefined)Requirements are All You Need: The Final Frontier for End-User Software EngineeringACM Transactions on Software Engineering and Methodology10.1145/3708524
https://dl.acm.org/doi/10.1145/3708524

View Options

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