arXiv:2607.02782v1 Announce Type: cross Abstract: Predictions by machine learning (ML) and artificial intelligence (AI) models are often received skeptically unless they are paired with intelligible explanations. In the context of just-in-time defect prediction, highlighting small portions of a software change (diff) -- beyond rule-based lints -- where risk may be concentrated has not yet been extensively investigated. In this work, we leverage attention weights from an LLM-based Diff Risk Score (DRS) model to highlight parts of a diff that the model focuses on when predicting risk. We aggrega
The increasing adoption of large language models in software development necessitates transparent explanations for their predictions, particularly in critical areas like code risk.
This research enhances trust and accountability in AI-driven software development by providing explainable AI for code change risk, crucial for enterprise adoption.
The ability to pinpoint specific risky code portions within a diff using LLM attention weights shifts the developer experience from opaque AI suggestions to actionable, interpretable insights.
- · Software developers
- · DevOps teams
- · Companies adopting AI for code quality
- · Explainable AI researchers
- · Companies with opaque AI code analysis tools
- · Traditional rule-based linting solutions
Developers can more effectively identify and mitigate risk in code changes, improving software quality and security.
Reduced incidence of defects and security vulnerabilities due to AI-guided code reviews leads to more robust software systems.
Increased public and institutional trust in AI's role in critical infrastructure development, fostering broader integration of AI in sensitive domains.
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Read at arXiv cs.AI