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Study reveals 3 key factors for productive ChatGPT code generation

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv explores how users interact with ChatGPT for code generation tasks, focusing on project-level complexities beyond simple function generation. The research involved 36 participants who used specific prompting strategies with ChatGPT, and their interactions were analyzed through screen recordings and chat logs. The study identified three key interaction features that consistently boosted productivity, proposed five guidelines for enhancing Human-LLM Interaction (HLI) in coding, and cataloged 29 common errors with suggested solutions. AI

IMPACT Identifies specific interaction strategies and guidelines to improve developer productivity when using LLMs for complex coding tasks.

RANK_REASON Research paper published on arXiv detailing a user study on LLM interaction for code generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Study reveals 3 key factors for productive ChatGPT code generation

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Research paper published on arXiv detailing a user study on LLM interaction for code generation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Sangwon Hyun, Hyunjun Kim, Jinhyuk Jang, Hyojin Choi, M. Ali Babar ·

    Experimental Analysis of Productive Interaction Strategy with ChatGPT: User Study on Function and Project-level Code Generation Tasks

    arXiv:2508.04125v2 Announce Type: replace-cross Abstract: The application of Large Language Models (LLMs) is growing in the productive completion of Software Engineering tasks. Yet, studies investigating productive prompting techniques often employed a limited problem space, focu…

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