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

2026-01-19 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

An ongoing mixed-methods user study explores the potential of personified code review explanations to address varying interpretations of feedback among developers. Initial findings from 16 participants, evenly split between "Abi" and "Tim" problem-solving personas and novice/expert experience levels, indicate that preferences for explanation styles differ significantly across problem-solving styles, experience levels, and roles. Specifically, novice-Abi participants consistently preferred Abi-aligned explanations, while expert-Tim participants favored Tim-aligned comments. Across all personas, developers valued explanatory depth, learning support, practical suggestions, and risk awareness more than conciseness. The study utilized ChatGPT (GPT-5.2) to generate persona-aligned comments. Most participants expressed positive views toward adaptive code review tools, noting potential improvements in clarity and efficiency, but also raised concerns about over-simplification and the need for transparency.

Key takeaway

For AI Engineers developing code review tools, recognize that generic feedback hinders communication. Your systems should adapt explanations based on developer problem-solving styles, experience, and roles, prioritizing explanatory depth and practical suggestions over conciseness. Implement persona-aware feedback, potentially using frameworks like GenderMag, to enhance clarity and efficiency, but ensure transparency and correctness to build trust and avoid over-simplification.

Key insights

Developer preferences for code review explanations vary significantly by problem-solving style, experience, and role.

Principles

• Explanatory depth and learning support are valued over conciseness.
• Persona-aligned feedback improves clarity and efficiency.
• Adaptive tools require balancing personalization with correctness.

Method

A mixed-methods user study assessed developer perceptions of ChatGPT (GPT-5.2)-generated, GenderMag persona-aligned code review comments across multiple snippets, categorizing preferences by problem-solving style, experience, and role.

In practice

• Tailor code review comments to developer problem-solving styles.
• Prioritize detailed explanations and learning support in feedback.
• Consider GenderMag personas for AI-assisted review systems.

Topics

• Code Review
• Persona-Aware AI
• Large Language Models
• GenderMag Framework
• Software Quality
• Human-Centered AI

Code references

• Open-Code-Crafters/FitFlex
• twentyhq/twenty

Best for: AI Scientist, Research Scientist, Software Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.