From Generic to Personalized: Exploring Persona-Aware Code Review Explanations

2026-07-09

AI Summary

This study addresses the inefficiencies in collaborative code review caused by mismatches in how developers interpret feedback. To bridge this gap, it introduces a personality-aware mechanism and employs a mixed-methods user study—combining persona modeling and qualitative analysis—to investigate personalized review feedback tailored to developers’ problem-solving styles, experience levels, and roles. Findings reveal that developers prefer feedback offering explanatory depth, learning support, actionable suggestions, and risk awareness over mere conciseness. By striking a balance between personalization and clarity with credibility, this work advances the development of human-centered AI-assisted code review systems.

Abstract

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.

Problem

• code review
• personalization
• developer personas
• communication
• explanation styles

Innovation

• persona-aware
• code review
• personalized explanations
• problem-solving styles
• human-centered AI