[2507.03156] The Impact of LLM-Assistants on Software Developer Productivity: A Systematic Review and Mapping Study

Computer Science > Software Engineering arXiv:2507.03156 (cs) [Submitted on 3 Jul 2025 (v1), last revised 23 Mar 2026 (this version, v2)] Title:The Impact of LLM-Assistants on Software Developer Productivity: A Systematic Review and Mapping Study Authors:Amr Mohamed, Maram Assi, Mariam Guizani View a PDF of the paper titled The Impact of LLM-Assistants on Software Developer Productivity: A Systematic Review and Mapping Study, by Amr Mohamed and 2 other authors View PDF HTML (experimental) Abstract:Large language model assistants (LLM-assistants) present new opportunities to transform software development. Developers are increasingly adopting these tools across tasks, including coding, testing, debugging, documentation, and design. Yet, despite growing interest, there is no synthesis of how LLM-assistants affect software developer productivity. In this paper, we present a systematic review and mapping of 39 peer-reviewed studies published between January 2014 and December 2024 that examine this impact. Our analysis reveals that the majority of studies report considerable benefits from LLM-assistants, though a notable subset identifies critical risks. Commonly reported gains include accelerated development, minimized code search, and the automation of trivial and repetitive tasks. However, studies also highlight concerns around cognitive offloading and reduced team collaboration. Our study reveals that whether LLM-based assistants improve or degrade code quality remains unre...