From Generic to Personalized: Exploring Persona-Aware Code Review Explanations
For developers and AI-assisted code review systems, this work identifies the need for personalized feedback to improve communication and collaboration.
This paper explores persona-aware code review explanations to address varying developer interpretations. Initial user study results show that preferences for explanation styles differ by problem-solving style, experience, and role, with developers valuing depth, learning support, and practical suggestions over conciseness.
Code review is essential for ensuring software quality and supporting collaboration, yet prior work shows that developers can interpret code review comments differently. These differences can hinder effective communication, particularly in collaborative settings. To address this challenge, we explore the potential of personified code review explanations. We report initial findings from an ongoing mixed-methods user study in which developers evaluated persona-aligned review comments across multiple code snippets. Our results suggest that preferences for explanation styles vary across problem-solving styles, experience levels, and roles. Across problem-solving style profiles, developers valued explanatory depth, learning support, practical suggestions, and risk awareness over conciseness, highlighting the need to balance personalization with clarity and trust. Based on these findings, we outline a vision for inclusive, human-centered AI-assisted code review systems that adapt feedback to developers' problem-solving preferences.