aiqbench analysis
This study provides empirical evidence on "vibe coding," revealing that while natural-language interaction with LLMs boosts development speed by 27%, it introduces significant trade-offs in code maintainability and security. The findings highlight a critical shift where reduced syntactic effort increases linguistic reasoning demands and risks associated with perceived loss of control over AI-generated outputs. Consequently, the research advocates for a hybrid framework that balances productivity gains with rigorous human oversight to ensure responsible adoption in software engineering workflows.
The Vibe Shift in Software Engineering: Evaluating AI-Led Conversational Programming for Performance, Cognition, and Responsible Adoption
arXiv:2609.09560v1 Announce Type: cross Abstract: This study evaluates Vibe Coding, an emerging AI-led conversational programming paradigm that enables developers to generate software through natural-language interaction with large language models. Using a mixed-methods design, the study assessed performance efficiency, cognitive implications, and responsible adoption in comparison with traditional and AI-assisted coding environments. Thirty participants, including professional developers and advanced computing students, completed equivalent programming tasks under three experimental conditions. Quantitative data were analyzed using descriptive statistics and repeated-measures ANOVA, while qualitative data were examined through thematic analysis. Results show that vibe coding significantly improved development efficiency, reducing task completion time by 27% compared with traditional coding and 12% compared with AI-assisted coding. However, these gains were accompanied by lower maintainability indices and higher security vulnerabilities, indicating trade-offs in software quality. Usability results yielded a good rating (SUS = 71.4), while cognitive workload remained moderate (NASA-TLX = 55.5), reflecting reduced syntactic effort but increased linguistic reasoning. Thematic analysis identified trust calibration, loss of control, cognitive adaptation, and prompt-engineering strategy as key constructs. Notably, perceived loss of control was associated with increased security risks due to reduced transparency and validation of AI-generated outputs. Based on these findings, the study proposes a three-pillar framework for responsible adoption: hybrid integration of human and AI capabilities, human oversight and transparent accountability, and context-aware deployment. Overall, vibe coding enhances productivity but requires critical oversight, reinforcing its role as a transformative yet transitional paradigm in software development.
Abstract:This study evaluates Vibe Coding, an emerging AI-led conversational programming paradigm that enables developers to generate software through natural-language interaction with large language models. Using a mixed-methods design, the study assessed performance efficiency, cognitive implications, and responsible adoption in comparison with traditional and AI-assisted coding environments. Thirty participants, including professional developers and advanced computing students, completed equivalent programming tasks under three experimental conditions. Quantitative data were analyzed using descriptive statistics and repeated-measures ANOVA, while qualitative data were examined through thematic analysis. Results show that vibe coding significantly improved development efficiency, reducing task completion time by 27% compared with traditional coding and 12% compared with AI-assisted coding. However, these gains were accompanied by lower maintainability indices and higher security vulnerabilities, indicating trade-offs in software quality. Usability results yielded a good rating (SUS = 71.4), while cognitive workload remained moderate (NASA-TLX = 55.5), reflecting reduced syntactic effort but increased linguistic reasoning. Thematic analysis identified trust calibration, loss of control, cognitive adaptation, and prompt-engineering strategy as key constructs. Notably, perceived loss of control was associated with increased security risks due to reduced transparency and validation of AI-generated outputs. Based on these findings, the study proposes a three-pillar framework for responsible adoption: hybrid integration of human and AI capabilities, human oversight and transparent accountability, and context-aware deployment. Overall, vibe coding enhances productivity but requires critical oversight, reinforcing its role as a transformative yet transitional paradigm in software development.
Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
arXiv:2609.09560 [cs.SE]
(or arXiv:2609.09560v1 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2609.09560 arXiv-issued DOI via DataCite (pending registration)
S. Aribe & L. J. Labastida, The Vibe Shift in Software Engineering: Evaluating AI-Led Conversational Programming for Performance, Cognition, & Responsible Adoption, Int. J. Adv. Sci. Eng. Inf. Technol., vol. 16, no. 4, pp. 1459-1472, 2026
https://doi.org/10.18517/ijaseit.16.4.21812 DOI(s) linking to related resources
From: Sales Aribe Jr. [view email] [v1] Wed, 9 Sep 2026 00:33:17 UTC (1,271 KB)
Full text under the publisher's open licence · 315 words
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