LLMs' Reshaping of People, Processes, Products, and Society in Software Development: A Comprehensive Exploration with Early Adopters

TL;DR

This study investigates how large language models reshape software development across people, processes, products, and society by interviewing 16 early adopters. It reveals a productivity–quality paradox: LLMs boost efficiency and learning but often produce outputs that require substantial verification and integration, shifting value from generation to critical evaluation. The findings highlight phase-dependent utility (high in design/implementation, limited in requirements), the emergence of new competencies (prompt engineering, verification, security-aware use), and the necessity of governance and education to realize responsible adoption. By comparing early adopter insights with subsequent literature, the work provides actionable guidance for developers, organizations, and educators to navigate LLM-enabled software practice while preserving core professional skills.

Abstract

Large language models (LLMs) are rapidly reshaping software development, but their impact across the software development lifecycle is underexplored. Existing work focuses on isolated activities such as code generation or testing, leaving open questions about how LLMs affect developers, processes, products, and the software ecosystem. We address this gap through semi-structured interviews with sixteen early-adopter software professionals who integrated LLM-based tools into their day-to-day work in early to mid-2023. We treat these interviews as early empirical evidence and compare participants' accounts with recent work on LLMs in software engineering, noting which early patterns persist or shift. Using thematic analysis, we organize findings around four dimensions: people, process, product, and society. Developers reported substantial productivity gains from reducing routine tasks, streamlining search, and accelerating debugging, but also described a productivity-quality paradox: they often discarded generated code and shifted effort from writing to critically evaluating and integrating it. LLM use was highly phase-dependent, with strong uptake in implementation and debugging but limited influence on requirements gathering and other collaborative work. Participants developed new competencies to use LLMs effectively, including prompt engineering strategies, layered verification, and secure integration to protect proprietary data. They anticipated changes in hiring expectations, team practices, and computing education, while emphasizing that human judgment and foundational software engineering skills remain essential. Our findings, later echoed in large-scale quantitative studies, offer actionable implications for developers, organizations, educators, and tool designers seeking to integrate LLMs responsibly into software practice today.