Maurice HT Ling*

HOHY PTE LTD, Singapore

*Corresponding Author: Maurice HT Ling, HOHY PTE LTD, Singapore.

Received: April 10, 2025; Published: April 15, 2025

Abstract

The term “vibe coding” was coined by Andrej Karpathy on 03 February 2025 and defined in Merriam Webster dictionary as “the practice of writing code, making web pages, or creating apps, by just telling an AI program what you want, and letting it create the product for you”. This may be the dream scenario of Frederick B. Thompson and Jean E. Sammet – both advocating English as a programming language in 1966, and I argue that vibe coding is its natural progression to today. For the purpose of this article, I tried the implementation of ProjEB (a command line interface electronic laboratory notebook) using vibe coding. Using Claude 3.5 Sonnet on GitHub Copilot within Microsoft VS Code, I produced working 994 lines of operational codes with 1685 lines of test codes within 28.5 hours from 161 lines of requirements as prompt. From this trial, I learnt that vibe coding is not exactly easy to accomplish as it substantially tapped on my previous experiences with software design and coding. I feel like a software architect or project manager having to envision the end result at the start, and to describe the vision to GitHub Copilot as the initial prompt. However, it does have enormous potential ahead and if this is the start of vibe coding, I am excited.

Keywords: Vibe Coding; AI-Assisted Coding; AI-Coding Assistant; Chatbot; Source Code Generation; Large Language Models; Coding Tools; Prompt Engineering

References

1. Hartman J., et al. “AI-Assisted Coding with Cody: Lessons from Context Retrieval and Evaluation for Code Recommendations”. 18^th ACM Conference on Recommender Systems (ACM, Bari Italy) (2024): 748-750.
2. Ramler R., et al. “Industrial Experience Report on AI-Assisted Coding in Professional Software Development”. Proceedings of the 1^st International Workshop on Large Language Models for Code (ACM, Lisbon Portugal) (2024): 1-7.
3. Martinović B and Rozić R. “Perceived Impact of AI-Based Tooling on Software Development Code Quality”. SN Computer Science1 (2025): 63.
4. Fan G., et al. “The impact of AI-assisted pair programming on student motivation, programming anxiety, collaborative learning, and programming performance: a comparative study with traditional pair programming and individual approaches”. International Journal of STEM Education1 (2025): 16.
5. Ramírez-Rueda R., et al. “Transforming Software Development: A Study on the Integration of Multi-Agent Systems and Large Language Models for Automatic Code Generation”. 2024 12th International Conference in Software Engineering Research and Innovation (CONISOFT) (IEEE, Puerto Escondido, Mexico) (2024): 11-20.
6. Thaler SL. “Virtual Input” Phenomena Within the Death of a Simple Pattern Associator”. Neural Networks1 (1995): 55-65.
7. Farquhar S., et al. “Detecting Hallucinations in Large Language Models Using Semantic Entropy”. Nature8017 (2024): 625-630.
8. Ganesh P., et al. “On the Role of Prompt Multiplicity in LLM Hallucination Evaluation”. ICLR 2025 Workshop on Building Trust in Language Models and Applications (2025).
9. Ji Z., et al. “Towards Mitigating LLM Hallucination via Self Reflection”. Findings of the Association for Computational Linguistics: EMNLP 2023 (Association for Computational Linguistics, Singapore) (2023): 1827-1843.
10. Ling MH. “ChatGPT (Feb 13 Version) is a Chinese Room”. Novel Research in Sciences2 (2023): NRS.000832.
11. Chelli M., et al. “Hallucination Rates and Reference Accuracy of ChatGPT and Bard for Systematic Reviews: Comparative Analysis”. Journal of Medical Internet Research 26 (2024): e53164.
12. Searle J. “Chinese Room Argument”. Scholarpedia8 (2009): 3100.
13. Abdalkareem R., et al. “What Do Developers Use the Crowd For? A Study Using Stack Overflow”. IEEE Software2 (2017): 53-60.
14. Kessel M and Atkinson C. “Code Search Engines for the Next Generation”. Journal of Systems and Software 215 (2024): 112065.
15. Di Grazia L and Pradel M. “Code Search: A Survey of Techniques for Finding Code”. ACM Computing Surveys11 (2023): 1-31.
16. Vaswani A., et al. “Attention is All You Need”. Advances in Neural Information Processing Systems 30 (2017).
17. Lo CK. “What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature”. Education Sciences4 (2023): 410.
18. Lee BC and Chung J. “An Empirical Investigation of the Impact of ChatGPT on Creativity”. Nature Human Behaviour10 (2024): 1906-1914.
19. Tangsrivimol JA., et al. “Benefits, Limits, and Risks of ChatGPT in Medicine”. Frontiers in Artificial Intelligence 8 (2025): 1518049.
20. Picazo-Sanchez P and Ortiz-Martin L. “Analysing the Impact of ChatGPT in Research”. Applied Intelligence5 (2024): 4172-4188.
21. A Shaji George., et al. “A Review of ChatGPT AI’s Impact on Several Business Sectors. Partners Universal International Innovation Journal1 (2023): 9-23.
22. Adamopoulou E and Moussiades L. “Chatbots: History, Technology, and Applications”. Machine Learning with Applications 2 (2020): 100006.
23. Millam A and Bakke C. “Coding with AI as an Assistant: Can AI Generate Concise Computer Code?” Journal of Information Technology Education: Innovations in Practice 23 (2024): Article 9.
24. Nah J-H. “Analysis of ChatGPT’s Coding Capabilities in Foundational Programming Courses”. Journal of Engineering Education Research6 (2023): 71-78.
25. Haindl P and Weinberger G. “Does ChatGPT Help Novice Programmers Write Better Code? Results From Static Code Analysis”. IEEE Access 12 (2024): 114146-114156.
26. Pinto G., et al. “Developer Experiences with a Contextualized AI Coding Assistant: Usability, Expectations, and Outcomes”. Proceedings of the IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI (ACM, Lisbon Portugal) (2024): 81-91.
27. Rose K., et al. “IS Professors’ Perspectives on AI-Assisted Programming”. Issues in Information Systems2 (2023).
28. Knowlton PH. “The Use of an Algebraic Language as Both a Source and Target Language”. Proceedings of the 1968 23rd ACM National Conference (ACM Press, Not Known) (1968): 787-794.
29. Antoy S., et al. “Specification-Based Code Generation”. Twenty-Third Annual Hawaii International Conference on System Sciences (IEEE Comput. Soc. Press, Kailua-Kona, HI, USA), 2 (1990): 165-173.
30. Domı´nguez E., et al. “A Systematic Review of Code Generation Proposals from State Machine Specifications”. Information and Software Technology10 (2012): 1045-1066.
31. Thompson FB. “English for the Computer”. Proceedings of the November 7-10, 1966, Fall Joint Computer Conference on XX - AFIPS ’66 (Fall) (ACM Press, San Francisco, California) (1966): 349.
32. Sammet JE. “The Use of English as a Programming Language”. Communications of the ACM3 (1966): 228-230.
33. Thompson FB., et al. “REL: A Rapidly Extensible Language system”. Proceedings of the 1969 24th National Conference On - (ACM Press, Not Known) (1969): 399-417.
34. Chamberlin DD and Boyce RF. “SEQUEL: A Structured English Query Language”. Proceedings of the 1976 ACM SIGFIDET (Now SIGMOD) Workshop on Data Description, Access and Control - FIDET ’76 (ACM Press, Not Known) (1976): 249-264.
35. Badiola KA., et al. “Experiences with a Researcher-Centric ELN”. Chemical Science3 (2015): 1614-1629.
36. Ng YY and Ling MH. “Electronic laboratory notebook on Web2Py framework”. The Python Papers3 (2010): 7.
37. Finnie-Ansley J., et al. “The Robots Are Coming: Exploring the Implications of OpenAI Codex on Introductory Programming”. Proceedings of the 24th Australasian Computing Education Conference (ACM, Virtual Event Australia) (2022): 10-19.
38. Bhattacharyya M., et al. “High Rates of Fabricated and Inaccurate References in ChatGPT-Generated Medical Content”. Cureus5 (2023): e39238.
39. Walters WH and Wilder EI. “Fabrication and Errors in the Bibliographic Citations Generated by ChatGPT”. Scientific Reports1 (2023): 14045.
40. Lechien JR., et al. “Accuracy of ChatGPT-3.5 and -4 in Providing Scientific References in Otolaryngology-Head and Neck Surgery”. European Archives of Oto-rhino-laryngology4 (2024): 2159-2165.
41. Ruta MR., et al. “ChatGPT for Univariate Statistics: Validation of AI-Assisted Data Analysis in Healthcare Research”. Journal of Medical Internet Research 27 (2025): e63550.
42. Prandner D., et al. “ChatGPT as a Data Analyst: An Exploratory Study on AI-Supported Quantitative Data Analysis in Empirical Research”. Frontiers in Education 9 (2025): 1417900.
43. Rasheed A., et al. “Requirement Engineering Challenges in Agile Software Development”. Mathematical Problems in Engineering 2021 (2021): 1-18.
44. Hussain A., et al. “The Role of Requirements in the Success or Failure of Software Projects”. International Review of Management and Marketing7 (2016): 306-311.
45. Liang JT., et al. “A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges”. 2024 IEEE/ACM 46th International Conference on Software Engineering (ICSE 2024) (ACM Press, Lisbon, Portugal) (2024).
46. Beck K. “Test Driven Development: By Example (‎Addison-Wesley Educational Publishers Inc)”. (2002).
47. George B and Williams L. “A Structured Experiment of Test-Driven Development. Information and Software Technology5 (2004): 337-342.
48. Loveland DW. “Automated Theorem Proving: Mapping Logic into AI”. Proceedings of the ACM SIGART International Symposium on Methodologies for Intelligent Systems, ISMIS ’86. (Association for Computing Machinery, New York, NY, USA) (1986): 214-229.
49. Negri-Ribalta C., et al. “A Systematic Literature Review on the Impact of AI Models on the Security of Code Generation”. Frontiers in Big Data 7 (2024): 1386720.
50. Nazir S., et al. “Modern Code Review Benefits - Primary Findings of A Systematic Literature Review”. Proceedings of the 3rd International Conference on Software Engineering and Information Management (ACM, Sydney NSW Australia) (2020): 210-215.
51. Braz L and Bacchelli A. “Software Security During Modern Code Review: The Developer’s Perspective”. Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ACM, Singapore Singapore) (2022): 810-821.
52. Wadhwa N., et al. “CORE: Resolving Code Quality Issues using LLMs”. Proceedings of the ACM on Software EngineeringFSE (2024): 789-811.
53. Light JS. “When Computers Were Women”. Technology and Culture3 (1999): 455-483.
54. Ceruzzi PE. “When Computers Were Human”. IEEE Annals of the History of Computing3 (1991): 237-244.

Citation

Citation: Maurice HT Ling. “Science/Education Portraits XI: The Advent of Vibe Coding".Acta Scientific Computer Sciences 7.1 (2025): 03-09.

Copyright

Copyright: © 2025 Maurice HT Ling. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.