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Explainable AI impacts developer trust and agreement in code reviews

A new study published on arXiv explores how Explainable AI (XAI) influences developer trust in AI-assisted code reviews. The research found that while full explanations led to higher perceived trust, they did not necessarily increase agreement with AI recommendations, suggesting developers may question AI suggestions more when explanations are provided. Conversely, moderate explanations resulted in the highest agreement rates, indicating a nuanced relationship between AI transparency and developer confidence in software development tools. AI

IMPACT Enhances understanding of how AI transparency affects developer trust and decision-making in software engineering workflows.

RANK_REASON The cluster centers on an academic paper detailing a user study about AI in software development.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Explainable AI impacts developer trust and agreement in code reviews

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The cluster centers on an academic paper detailing a user study about AI in software development.
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76 days old
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Zhenhan Gao, Marvin Mu\~noz Bar\'on, Umm-e Habiba, Daniel Graziotin, Stefan Wagner ·

    Evaluating the Impact of Explainable AI on Trust in AI-Assisted Code Review

    arXiv:2607.24601v1 Announce Type: cross Abstract: Background: Large language models (LLMs) are increasingly used to automate code review, but the reasoning behind their decisions remains hard to understand. Developers struggle to assess the validity of LLM-generated reviews, maki…

  2. dev.to — MCP tag TIER_1 English(EN) · HyperNexus ·

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