The aim of this study is to evaluate the performance of AI-assisted programming in actual mobile development teams that are focused on native mobile languages like Kotlin and Swift. The extensive case study involves 16 participants and 2 technical reviewers, from a software development department designed to understand the impact of using LLMs trained for code generation in specific phases of the team, more specifically, technical onboarding and technical stack switch. The study uses technical problems dedicated to each phase and requests solutions from the participants with and without using AI-Code generators. It measures time, correctness, and technical integration using ReviewerScore, a metric specific to the paper and extracted from actual industry standards, the code reviewers of merge requests. The output is converted and analyzed together with feedback from the participants in an attempt to determine if using AI-assisted programming tools will have an impact on getting developers onboard in a project or helping them with a smooth transition between the two native development environments of mobile development, Android and iOS. The study was performed between May and June 2023 with members of the mobile department of a software development company based in Cluj-Napoca, with Romanian ownership and management.

翻译：本研究旨在评估AI辅助编程在专注于原生移动语言（如Kotlin和Swift）的实际移动开发团队中的表现。该大型案例研究涉及来自软件开发部门的16名参与者及2名技术评审员，旨在探究使用专为代码生成而训练的大语言模型对团队特定阶段（具体为技术入职与技术栈切换）的影响。研究针对各阶段提出技术问题，并要求参与者在启用与未启用AI代码生成器的情况下提供解决方案。通过评审者得分（ReviewerScore）这一源自论文且从行业标准（合并请求的代码评审）中提取的指标，对时间、正确性及技术集成度进行度量。研究将输出结果与参与者的反馈相结合，进行转化与分析，以判断使用AI辅助编程工具是否有助于开发者快速上手项目，或促进其在移动开发两大原生环境（Android与iOS）间的平滑过渡。本研究于2023年5月至6月期间开展，研究对象为总部位于克卢日-纳波卡、由罗马尼亚资本与管理团队运营的一家软件开发公司的移动部门成员。