Related papers: Prompting in Practice: Investigating Software Developers' Use of Generative AI Tools

Prompting in Practice: Investigating Software Developers' Use of Generative AI Tools

URL: http://arxiv.org/abs/2510.06000v1
Date: Tue, 07 Oct 2025 15:02:22 GMT
Title: Prompting in Practice: Investigating Software Developers' Use of Generative AI Tools
Authors: Daniel Otten, Trevor Stalnaker, Nathan Wintersgill, Oscar Chaparro, Denys Poshyvanyk,
Abstract summary: The integration of generative artificial intelligence (GenAI) tools has fundamentally transformed software development.<n>This study presents a systematic investigation of how software engineers integrate GenAI tools into their professional practice.<n>We surveyed 91 software engineers, including 72 active GenAI users, to understand AI usage patterns throughout the development process.
Score: 17.926187565860232
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: The integration of generative artificial intelligence (GenAI) tools has fundamentally transformed software development. Although prompt engineering has emerged as a critical skill, existing research focuses primarily on individual techniques rather than software developers' broader workflows. This study presents a systematic investigation of how software engineers integrate GenAI tools into their professional practice through a large-scale survey examining prompting strategies, conversation patterns, and reliability assessments across various software engineering tasks. We surveyed 91 software engineers, including 72 active GenAI users, to understand AI usage patterns throughout the development process. Our 14 key findings show that while code generation is nearly universal, proficiency strongly correlates with using AI for more nuanced tasks such as debugging and code review, and that developers prefer iterative multi-turn conversations to single-shot prompting. Documentation tasks are perceived as most reliable, while complex code generation and debugging present sizable challenges. Our insights provide an empirical baseline of current developer practices, from simple code generation to deeper workflow integration, with actionable insights for future improvements.

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