Context: Code review has long been a core practice in collaborative software engineering. As automation becomes increasingly embedded in development workflows, the role and functioning of code review are subject to change. Objective: This study explores how professional developers anticipate the evolution of code review and identifies emerging tensions reflected in these expectations. Method: We conducted a cross-sectional survey with 100 developers across five software-driven companies. The survey captured estimates of current review time and reviewed artifacts, as well as anticipated changes over a five-year horizon. Open-ended questions invited reflections on the future of code review. Quantitative responses were analyzed descriptively, and open-ended responses were independently coded by multiple researchers using thematic analysis to identify recurring patterns in participant responses. Results: Practitioners expect code review to remain essential, anticipating stable or increased time investment and a broader range of reviewed artifacts over the next five years. In open-ended responses, many participants explicitly referenced AI and large language models (LLMs), describing increasing automation in both code authoring and reviewing, including scenarios in which automated systems operate in both roles. Conclusion: Our analysis suggests emerging tensions concerning understanding, accountability, and trust in automation-mediated code review. These tensions provide early empirical signals of socio-technical challenges and position code review as a concrete setting for examining the implications of LLM integration in collaborative software engineering.

翻译：背景：代码评审长期以来一直是协作式软件工程的核心实践。随着自动化日益嵌入开发工作流程，代码评审的角色与功能正在发生改变。目标：本研究探讨专业开发者如何预见代码评审的演变，并识别这些预期中反映出的新兴矛盾。方法：我们对五家软件驱动型公司的100名开发者进行了横截面调查。调查收集了当前评审时间与评审工件的估算数据，以及对未来五年内可能变化的预期。开放性题目邀请参与者对代码评审的未来进行反思。定量数据进行描述性分析，开放性回答由多位研究者独立编码，采用主题分析法识别参与者回答中的重复模式。结果：从业者预期代码评审仍将至关重要，预计未来五年内时间投入保持稳定或增加，且评审工件的范围将扩大。在开放性回答中，许多参与者明确提及人工智能与大语言模型（LLM），描述了代码编写与评审中日益增长的自动化——包括自动化系统同时担任两个角色的场景。结论：我们的分析揭示了自动化中介的代码评审中关于理解、问责与信任的新兴矛盾。这些矛盾为技术社会性挑战提供了早期经验性信号，并将代码评审定位为检验大语言模型集成对协作式软件工程影响的具体场景。