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

[2503.05012] LLMs' Reshaping of People, Processes, Products, and Society in Software Development: A Comprehensive Exploration with Early Adopters Skip to main content Search Submit Donate Log in Computer Science > Software Engineering arXiv:2503.05012 (cs) [Submitted on 6 Mar 2025 ( v1 ), last revised 23 Nov 2025 (this version, v2)] Title: LLMs' Reshaping of People, Processes, Products, and Society in Software Development: A Comprehensive Exploration with Early Adopters Authors: Benyamin Tabarsi , Heidi Reichert , Sam Gilson , Ally Limke , Sandeep Kuttal , Tiffany Barnes View PDF HTML (experimental) 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. Subjects: Software Engineering (cs.SE) ; Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC) Cite as: arXiv:2503.05012 [cs.SE] (or arXiv:2503.05012v2 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2503.05012 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Empirical Software Engineering, 32, 21 (2027) Related DOI : https://doi.org/10.1007/s10664-026-10917-0 Focus to learn more DOI(s) linking to related resources Submission history From: Benyamin Tabarsi [ view email ] [v1] Thu, 6 Mar 2025 22:27:05 UTC (1,206 KB) [v2] Sun, 23 Nov 2025 07:25:07 UTC (386 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: cs.SE < prev | next > new | recent | 2025-03 Change to browse by: cs cs.AI cs.HC References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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