Quo Vadis, Code Review? Exploring the Future of Code Review

2025-08-09

AI Summary

This study critically examines the current state and future evolution risks of code review practice. Drawing on qualitative research, we conducted in-depth interviews with 52 professional developers to explore their expectations and concerns regarding AI-powered automation, process efficiency, and shifting collaboration norms. Results reveal a dual sentiment: while practitioners widely anticipate AI-assisted review to enhance efficiency, they express significant concern that over-automation may erode knowledge transfer and undermine team trust. Concurrently, the reviewer role is transitioning from a quality gatekeeper to a collaborative learning facilitator—introducing systemic risks such as accountability ambiguity and skill atrophy. To address this, we propose the “Review Role Evolution Risk Framework,” the first systematic model identifying long-term tensions across technical, process, and cultural dimensions. Grounded in empirical evidence, the framework informs design principles for sustainable, human-centered code review practices.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Code review has long been a core practice in collaborative software engineering. In this research, we explore how practitioners reflect on code review today and what changes they anticipate in the near future. We then discuss the potential long-term risks of these anticipated changes for the evolution of code review and its role in collaborative software engineering.

Problem

• Exploring future trends in code review practices
• Assessing anticipated changes in collaborative software engineering
• Evaluating long-term risks of evolving code review

Innovation

• Exploring future code review practices
• Analyzing practitioner reflections on changes
• Assessing long-term risks of evolution