Adapting Code Review Processes to the Conditions of AI-Assisted Software Development - Bezhentsev, Yurii (European Journal of Science, Innovation and Technology, 2026)
The article presents an analysis of the transformation of code review processes in the context of software development with the use of artificial intelligence. The study is conducted in the format of a systematic review and analytical synthesis of academic publications focusing on code generation, development productivity, and the organization of change validation processes. The primary focus is on the relationship between the speed of code creation, the characteristics of introduced changes, and the ability of the review process to ensure their reliable interpretation and safe integration. The key parameters determining the effectiveness of code review are examined, including temporal characteristics, the structure of changes, and cognitive load. It is established that the impact of code generation tools is indirect and manifests through the formation of asymmetry between the intensity of generation and the capacity for review. It is shown that even with improvements in the quality of automatically generated code, the risk of uncritical acceptance and the integration of hidden logical inconsistencies remains. An original adaptive code review model is proposed, reflecting a transition from single-stage review to a multi-level system that includes preliminary filtering, structured analysis, risk assessment, and a feedback loop. The results obtained allow code review to be considered as a controllable risk regulation mechanism determining the stability and predictability of the development process. The practical significance of the study lies in substantiating the need to structure code review processes, limit the scope of changes, and increase transparency in the origin of code fragments. The article will be useful for software developers, engineering managers, and specialists in software process management.