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辅助编程在实际移动开发团队中的表现，这些团队专注于Kotlin和Swift等原生移动语言。这项大规模案例研究涉及来自软件开发部门的16名参与者和2名技术评审员，旨在探究使用针对代码生成训练的LLM（大型语言模型）对团队特定阶段（特别是技术入职和技术栈转换）的影响。研究针对每个阶段设计了技术问题，要求参与者在是否使用AI代码生成器的情况下分别提出解决方案。通过时间、正确性和技术整合度三个维度进行评估，其中技术整合度采用论文特有的度量标准ReviewerScore（该指标从行业实际标准中提取，即合并请求的代码评审员评分）。研究将输出结果进行转换，并结合参与者反馈进行分析，以确定使用AI辅助编程工具是否能帮助开发者快速融入项目，或协助他们在移动开发的两个原生环境（Android和iOS）之间实现平稳过渡。该研究于2023年5月至6月进行，研究对象为罗马尼亚人所有和管理的克卢日-纳波卡某软件开发公司移动部门的成员。