Aug 2026· International Journal For Multidisciplinary Research· 0 citations· 11 references
TL;DR
The study concludes that while Vibe Coding is optimal for isolated prototyping, it introduces systemic risks in mission-critical architecture and mandates a pivot in software engineering pedagogy from syntax authorship to rigorous forensic curation and adversarial auditing.
Abstract
The software engineering landscape is undergoing a radical transition from manual syntax craftsmanship to "Vibe Coding"a paradigm defined by prompt-driven, intent-based software generation. This research quantifies the impact of this shift on the software development lifecycle (SDLC), specifically evaluating the efficacy of code review and debugging. Utilizing a diagnostic pipeline with a 50-developer cohort, this study compares traditional Integrated Development Environment (IDE) workflows against AI-assisted "vibe" workflows (utilizing GitHub Copilot and ChatGPT). Our findings demonstrate a profound "Time Displacement Penalty." While initial development velocity approaches zero, severe debugging penalties and elevated bug escape rates in complex systems fundamentally offset these early gains. We identify a "Cognitive Void" wherein developers exhibit high speed and syntax correctness but suffer from a measurable detachment from system architecture and forensic comprehension. This detachment facilitates "Automation Bias" during peer reviews, where the aesthetic perfection of AI-generated syntax masks deep, hallucinated logic failures. The study concludes that while Vibe Coding is optimal for isolated prototyping, it introduces systemic risks in mission-critical architecture. The data mandates a pivot in software engineering pedagogy from syntax authorship to rigorous forensic curation and adversarial auditing.
AI-generated C++ code has a distinct quality profile, showing higher rates of interface and coupling burdens, copy and allocation overheads, and a reliance on explicit loops over optimized standard APIs, which translates into tangible downstream costs, including increased review effort and a 5-8% increase in compute resource consumption.
Michael Tran, Fred Lewis, Kun Yang et al.· 1 citation
This study compares the structural quality of code produced by three widely adopted vibe coding tools --- Lovable, v0, and Replit --- starting from a single generation prompt and suggests that choosing between vibe coding tools involves structural trade-offs that go beyond perceived productivity.
Investigating LLMs as metric-driven refactoring assistants rather than code generators suggests that while LLMs are valuable assistants for structural improvement, their interventions require careful monitoring to avoid unintended trade-offs.
Tindwende Thierry Sawadogo, Fadel Touré· International Conference on...· 0 citations
The research found out three things about how developers use Artificial Intelligence, adoption of Artificial Intelligence satisfaction, and with Artificial Intelligence the different ways developers are using Artificial Intelligence is changing.
P. Vijayakumar, Jegatheeswari Perumalsamy, P. Parida et al.· Proceedings of the 1st Inter...· 0 citations
Continuous Integration and Continuous Delivery (CI/CD) pipelines accelerate software evolution but complicate the timely assessment of structural degradation. While code smells effectively indicate maintainability risks, their detection remains largely confined to isolated static analysis rather than being actively integrated into continuous workflows. To address this gap, we propose an explainable AI-driven approach for code smell classification designed specifically for software quality risk analysis in CI/CD environments. Using structural metrics related to complexity, coupling, and cohesion, we train a Random Forest model to classify four prevalent smells: Blob, Long Method, Feature Envy, and Data Class. Evaluated on labeled Java systems, our approach successfully captures the structural patterns indicative of software decay, achieving 82% accuracy and a 0.86 macro F1-score. Beyond raw performance, we operationalize these classifications as interpretable risk indicators within a prototype CI/CD pipeline, prioritizing human-centered evaluation. Ultimately, this work advances the AI for Software Engineering (AI4SE) domain by demonstrating how explainable machine learning can practically augment continuous quality assurance in agile development.
J. Domingues, D. Duarte, António Sousa et al.· International Conference on...· 0 citations
The paper is trying to deeply analyze the pros and cons of implementing GenAI into software development, analysing current applications used in software development life cycle (SDLC), drawing upon case studies and programmer experience, and examining effect on code quality, team working and project timeline.
P. Arun, Jagdale, Deepti Ameta et al.· Economic Sciences· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
