Evaluating Vibe Coding as an AI-Orchestrated Development Methodology: A Case Study on Accelerating Complex Web-Based Educational Management Systems
DOI:
https://doi.org/10.35870/ijsecs.v6i1.6823Keywords:
Vibe Coding, AI-Assisted Development, Model Context Protocol, Human-in-the-Loop, Software Productivity, Case StudyAbstract
The emergence of generative AI has disrupted conventional software development practices, prompting considerable skepticism among IT professionals about whether such tools displace rather than augment human expertise. This study introduces "Vibe Coding" as a collaborative methodology — one in which AI operates as a capable partner, not a substitute — requiring human guidance for review, analysis, and iterative refinement of generated outputs; the primary objective is to assess whether Vibe Coding, when structured through Model Context Protocol (MCP) and schema engineering, can materially reduce development time for complex web systems — including CRUD operations, API integration, and custom business logic — relative to conventional approaches such as Waterfall. Two research questions drive the inquiry: (1) Can Vibe Coding compress development timelines for complex systems from months to days? and (2) How effective is AI as a collaborative partner in sustaining output quality through human-in-the-loop validation? A single case study approach was employed, applying the methodology to develop an ISO 9001:2015-compliant Management Information System (MIS) for Pondok Pesantren Abu Hurairah Mataram as a solo developer project, with metrics tracked across seven days including total development time, time per phase (planning, development, debugging, and deployment), proportion of AI-generated code (70–85%), prompt and iteration counts, bug frequency, debugging duration, total lines of code (LOC), and feature implementation success rate. Results show a completed system in seven days, with 70–85% of the codebase AI-generated and 15–30% manually refined for business logic, debugging, and performance tuning; human intervention effectively countered AI hallucinations throughout, repositioning the developer's role from syntax-level coding toward architectural orchestration and quality control. These findings suggest Vibe Coding raises productivity for solo developers in AI-saturated environments, though rigorous human oversight remains non-negotiable for production-grade systems.
Downloads
References
Agarwal, V., Pei, Y., Alamir, S., & Liu, X. (2024). CodeMirage: Hallucinations in code generated by large language models. arXiv. https://doi.org/10.48550/arXiv.2408.08333
Anthropic. (2024, November 25). Introducing the Model Context Protocol. https://www.anthropic.com/news/model-context-protocol
Brynjolfsson, E., Li, D., & Raymond, L. (2025). Generative AI at work. The Quarterly Journal of Economics, 140(2), 889–942. https://doi.org/10.1093/qje/qjae044
Daniotti, S., Wachs, J., Feng, X., & Neffke, F. (2026). Who is using AI to code? Global diffusion and impact of generative AI. Science, eadz9311. https://doi.org/10.1126/science.adz9311
DORA (Google Cloud). (2025). State of AI-assisted software development report. Google Cloud.
González, E. A., Rothkopf, R., Lerner, S., & Polikarpova, N. (2025). HiLDe: Intentional code generation via human-in-the-loop decoding. In Proceedings of the 2025 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) (pp. 222–233). IEEE. https://doi.org/10.1109/VL-HCC65237.2025.00032
Hou, X., Zhao, Y., Wang, S., & Wang, H. (2025). Model Context Protocol (MCP): Landscape, security threats, and future research directions. arXiv. https://arxiv.org/abs/2503.23278
Index.dev. (2025). Developer productivity statistics with AI tools. https://www.index.dev/blog/developer-productivity-statistics-with-ai-tools
International Organization for Standardization. (2015). ISO 9001:2015 quality management systems — Requirements. https://www.iso.org/standard/62085.html
Iqbal, M. A. (2026). SIM URT PAH - Mataram: Vibe Coding MIS implementation [Source code]. GitHub. https://github.com/iqbaladiatma/sim-pah
Karpathy, A. [@karpathy]. (2025, February). There's a new kind of coding I call "vibe coding"... [Post]. X. https://x.com/karpathy/status/1886192184808149383
Laravel. (2026). The PHP framework for web artisans. Retrieved March 17, 2026, from https://laravel.com
Niclavose, S. (2025). AI-driven software development: Opportunities and good practices [Master's thesis, Malmö University]. DiVA Portal. https://www.diva-portal.org/smash/record.jsf?pid=diva2:1996184
Peng, S., Kalliamvakou, E., Cihon, P., & Demirer, M. (2023). The impact of AI on developer productivity: Evidence from GitHub Copilot. arXiv. https://doi.org/10.48550/arXiv.2302.06590
Ray, P. P. (2025, March). Vibe coding: The complete guide to AI-powered development in 2025–2026. Medium. https://medium.com/@basukori8463/vibe-coding-the-complete-guide-to-ai-powered-development-in-2025-26-7810d2136dc2
Runeson, P., Höst, M., Rainer, A., & Regnell, B. (2012). Case study research in software engineering: Guidelines and examples. Wiley.
Thoughtworks. (2025). From vibe coding to context engineering: Technology radar 2025. https://www.thoughtworks.com/radar
Vaithilingam, P., Zhang, T., & Glassman, E. L. (2022, April). Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In CHI Conference on Human Factors in Computing Systems — Extended Abstracts (pp. 1–7). ACM. https://doi.org/10.1145/3491101.3519665
Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). Sage.
Zhang, Z., Wang, C., Wang, Y., Shi, E., Ma, Y., Zhong, W., & Zheng, Z. (2025). LLM hallucinations in practical code generation: Phenomena, mechanism, and mitigation. Proceedings of the ACM on Software Engineering, 2(ISSTA), 481–503. https://doi.org/10.1145/3728894.
Downloads
Published
Issue
Section
Categories
License
Copyright (c) 2026 Iqbal Muhammad Adiatma

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.
