Vibe coding boosts software speed but risks code quality issues
The Vibe Shift in Software Engineering: Evaluating AI-Led Conversational Programming for Performance, Cognition, and Responsible Adoption
Software EngineeringArtificial IntelligenceEmerging Technologies
Summary
Programming with AI through natural language, called vibe coding, helps developers write code faster than traditional or AI-assisted methods. However, this speed comes with more mistakes in maintaining and securing the software. The team found that while people felt easier about the syntax, they had to work harder to make sure the AI's code was right and safe. They suggest combining human skills with AI and keeping close oversight to use vibe coding responsibly.
What this means in practice
- •For professional software developers: Speed up coding by using conversational AI while organizing oversight to catch potential security and maintainability flaws.
- •For enterprise software teams: Integrate vibe coding in development pipelines with protocols for human review to balance efficiency and software quality.
- •For business automation designers: Create AI-powered tools that allow natural-language software creation for automating business processes with human oversight.$Commercial implications: Enables development of products that enable business users to generate software workflows via conversational AI with human quality checks.
Authors
Sales G. Aribe, Louie Jay S. Labastida
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.