Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
194 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Aptly: Making Mobile Apps from Natural Language (2405.00229v2)

Published 30 Apr 2024 in cs.HC, cs.AI, and cs.PL

Abstract: This paper introduces Aptly, a platform designed to democratize mobile app development, particularly for young learners. Aptly integrates a LLM with App Inventor, enabling users to create apps using their natural language. User's description is translated into a programming language that corresponds with App Inventor's visual blocks. A preliminary study with high school students demonstrated the usability and potential of the platform. Prior programming experience influenced how users interact with Aptly. Participants identified areas for improvement and expressed a shift in perspective regarding programming accessibility and AI's role in creative endeavors.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (33)
  1. P. M. Napoli and J. A. Obar, “The emerging mobile internet underclass: A critique of mobile internet access,” The Information Society, vol. 30, no. 5, p. 323–334, oct 2014. [Online]. Available: https://doi.org/10.1080/01972243.2014.944726
  2. D. Wolber, H. Abelson, and M. Friedman, “Democratizing computing with app inventor,” GetMobile: Mobile Comp. and Comm., vol. 18, no. 4, p. 53–58, jan 2015. [Online]. Available: https://doi.org/10.1145/2721914.2721935
  3. S. N. H. Mohamad, A. Patel, R. Latih, Q. Qassim, L. Na, and Y. Tew, “Block-based programming approach: challenges and benefits,” in Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, 2011, pp. 1–5.
  4. M. Tissenbaum, J. Sheldon, and H. Abelson, “From computational thinking to computational action,” Communications of the ACM, vol. 62, no. 3, pp. 34–36, 2019.
  5. S. Sarsa, P. Denny, A. Hellas, and J. Leinonen, “Automatic generation of programming exercises and code explanations using large language models,” in Proceedings of the 2022 ACM Conference on International Computing Education Research - Volume 1, ser. ICER ’22.   New York, NY, USA: Association for Computing Machinery, 2022, p. 27–43. [Online]. Available: https://doi.org/10.1145/3501385.3543957
  6. H. Su, J. Ai, D. Yu, and H. Zhang, “An evaluation method for large language models’ code generation capability,” in 2023 10th International Conference on Dependable Systems and Their Applications (DSA), 2023, pp. 831–838.
  7. X. Deng, “Group collaboration with App Inventor,” M. Eng. thesis, Massachusetts Institute of Technology, 2017.
  8. T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of the 34th International Conference on Neural Information Processing Systems, ser. NIPS ’20.   Red Hook, NY, USA: Curran Associates Inc., 2020.
  9. J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat et al., “Gpt-4 technical report,” arXiv preprint arXiv:2303.08774, 2023.
  10. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Proceedings of the 31st International Conference on Neural Information Processing Systems, ser. NIPS’17.   Red Hook, NY, USA: Curran Associates Inc., 2017, p. 6000–6010.
  11. L. Reynolds and K. McDonell, “Prompt programming for large language models: Beyond the few-shot paradigm,” in Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, ser. CHI EA ’21.   New York, NY, USA: Association for Computing Machinery, 2021. [Online]. Available: https://doi.org/10.1145/3411763.3451760
  12. J. Pennington, R. Socher, and C. Manning, “GloVe: Global vectors for word representation,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), A. Moschitti, B. Pang, and W. Daelemans, Eds.   Doha, Qatar: Association for Computational Linguistics, Oct. 2014, pp. 1532–1543. [Online]. Available: https://aclanthology.org/D14-1162
  13. A. Neelakantan, T. Xu, R. Puri, A. Radford, J. M. Han, J. Tworek, Q. Yuan, N. Tezak, J. W. Kim, C. Hallacy et al., “Text and code embeddings by contrastive pre-training,” arXiv preprint arXiv:2201.10005, 2022.
  14. K. Zhang and D. Shasha, “Simple fast algorithms for the editing distance between trees and related problems,” SIAM journal on computing, vol. 18, no. 6, pp. 1245–1262, 1989.
  15. E. Patton, “A look at component usage in MIT App Inventor,” 2020. [Online]. Available: https://appinventor.mit.edu/blogs/evan/2020/12/20/component-usage-mit-app-inventor
  16. A. Solar-Lezama, “Program synthesis by sketching,” Ph.D. dissertation, University of California at Berkeley, USA, 2008, aAI3353225.
  17. A. Kumar and P. Sharma, “Open ai codex: An inevitable future?” International Journal for Research in Applied Science and Engineering Technology, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:256889504
  18. D. M. Yellin, “The premature obituary of programming,” Commun. ACM, vol. 66, no. 2, p. 41–44, jan 2023. [Online]. Available: https://doi.org/10.1145/3555367
  19. C. Bull and A. Kharrufa, “Generative artificial intelligence assistants in software development education: A vision for integrating generative artificial intelligence into educational practice, not instinctively defending against it,” IEEE Software, vol. 41, no. 2, pp. 52–59, 2024.
  20. M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. de Oliveira Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman, A. Ray, R. Puri, G. Krueger, M. Petrov, H. Khlaaf, G. Sastry, P. Mishkin, B. Chan, S. Gray, N. Ryder, M. Pavlov, A. Power, L. Kaiser, M. Bavarian, C. Winter, P. Tillet, F. P. Such, D. Cummings, M. Plappert, F. Chantzis, E. Barnes, A. Herbert-Voss, W. H. Guss, A. Nichol, A. Paino, N. Tezak, J. Tang, I. Babuschkin, S. Balaji, S. Jain, W. Saunders, C. Hesse, A. N. Carr, J. Leike, J. Achiam, V. Misra, E. Morikawa, A. Radford, M. Knight, M. Brundage, M. Murati, K. Mayer, P. Welinder, B. McGrew, D. Amodei, S. McCandlish, I. Sutskever, and W. Zaremba, “Evaluating large language models trained on code,” 2021.
  21. N. Nguyen and S. Nadi, “An empirical evaluation of github copilot’s code suggestions,” in Proceedings of the 19th International Conference on Mining Software Repositories, ser. MSR ’22.   New York, NY, USA: Association for Computing Machinery, 2022, p. 1–5. [Online]. Available: https://doi.org/10.1145/3524842.3528470
  22. P. Denny, V. Kumar, and N. Giacaman, “Conversing with copilot: Exploring prompt engineering for solving cs1 problems using natural language,” Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1, 2022. [Online]. Available: https://api.semanticscholar.org/CorpusID:253157479
  23. A. Moradi Dakhel, V. Majdinasab, A. Nikanjam, F. Khomh, M. C. Desmarais, and Z. M. J. Jiang, “Github copilot ai pair programmer: Asset or liability?” J. Syst. Softw., vol. 203, no. C, sep 2023. [Online]. Available: https://doi.org/10.1016/j.jss.2023.111734
  24. S. Imai, “Is github copilot a substitute for human pair-programming? an empirical study,” in 2022 IEEE/ACM 44th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), 2022, pp. 319–321.
  25. B. Yetistiren, I. Ozsoy, and E. Tuzun, “Assessing the quality of github copilot’s code generation,” in Proceedings of the 18th International Conference on Predictive Models and Data Analytics in Software Engineering, ser. PROMISE 2022.   New York, NY, USA: Association for Computing Machinery, 2022, p. 62–71. [Online]. Available: https://doi.org/10.1145/3558489.3559072
  26. S. Lunn, M. Marques Samary, and A. Peterfreund, “Where is computer science education research happening?” in Proceedings of the 52nd ACM Technical Symposium on Computer Science Education, 2021, pp. 288–294.
  27. T. Rietz and A. Maedche, “Cody: An ai-based system to semi-automate coding for qualitative research,” Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 2021.
  28. R. Puri, D. S. Kung, G. Janssen, W. Zhang, G. Domeniconi, V. Zolotov, J. Dolby, J. Chen, M. Choudhury, L. Decker et al., “Codenet: A large-scale ai for code dataset for learning a diversity of coding tasks,” arXiv preprint arXiv:2105.12655, 2021.
  29. M. Hasan, K. S. Mehrab, W. U. Ahmad, and R. Shahriyar, “Text2app: A framework for creating android apps from text descriptions,” arXiv preprint arXiv:2104.08301, 2021.
  30. M. Welsh, “The end of programming,” Communications of the ACM, vol. 66, pp. 34 – 35, 2022.
  31. E. Klopfer, J. Reich, H. Abelson, and C. Breazeal, “Generative AI and K-12 Education: An MIT Perspective,” An MIT Exploration of Generative AI, mar 27 2024, https://mit-genai.pubpub.org/pub/4k9msp17.
  32. M. Johnson, “Generative ai and cs education,” Commun. ACM, vol. 67, no. 4, p. 23–24, mar 2024. [Online]. Available: https://doi.org/10.1145/3632523
  33. J. K. Eshraghian, “Human ownership of artificial creativity,” Nature Machine Intelligence, vol. 2, no. 3, pp. 157–160, 2020.
Citations (1)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets