Open Access

ARTICLE

Redefining the Programmer: Human-AI Collaboration, LLMs, and Security in Modern Software Engineering

Elyson De La Cruz^*, Hanh Le, Karthik Meduri, Geeta Sandeep Nadella^*, Hari Gonaygunta

School of Computer and Information Sciences, University of the Cumberlands, Williamsburg, KY 40769, USA

* Corresponding Authors: Elyson De La Cruz. Email: ; Geeta Sandeep Nadella. Email:

Computers, Materials & Continua 2025, 85(2), 3569-3582. https://doi.org/10.32604/cmc.2025.068137

Received 21 May 2025; Accepted 06 August 2025; Issue published 23 September 2025

Abstract

The rapid integration of artificial intelligence (AI) into software development, driven by large language models (LLMs), is reshaping the role of programmers from traditional coders into strategic collaborators within Industry 4.0 ecosystems. This qualitative study employs a hermeneutic phenomenological approach to explore the lived experiences of Information Technology (IT) professionals as they navigate a dynamic technological landscape marked by intelligent automation, shifting professional identities, and emerging ethical concerns. Findings indicate that developers are actively adapting to AI-augmented environments by engaging in continuous upskilling, prompt engineering, interdisciplinary collaboration, and heightened ethical awareness. However, participants also voiced growing concerns about the reliability and security of AI-generated code, noting that these tools can introduce hidden vulnerabilities and reduce critical engagement due to automation bias. Many described instances of flawed logic, insecure patterns, or syntactically correct but contextually inappropriate suggestions, underscoring the need for rigorous human oversight. Additionally, the study reveals anxieties around job displacement and the gradual erosion of fundamental coding skills, particularly in environments where AI tools dominate routine development tasks. These findings highlight an urgent need for educational reforms, industry standards, and organizational policies that prioritize both technical robustness and the preservation of human expertise. As AI becomes increasingly embedded in software engineering workflows, this research offers timely insights into how developers and organizations can responsibly integrate intelligent systems to promote accountability, resilience, and innovation across the software development lifecycle.

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Keywords

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