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arXiv · 2505.19757

CIDRe: A Reference-Free Multi-Aspect Criterion for Code Comment Quality Measurement

Maria Dziuba·Valentin Malykh
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Abstract

Effective generation of structured code comments requires robust quality metrics for dataset curation, yet existing approaches (SIDE, MIDQ, STASIS) suffer from limited code-comment analysis. We propose CIDRe, a language-agnostic reference-free quality criterion combining four synergistic aspects: (1) relevance (code-comment semantic alignment), (2) informativeness (functional coverage), (3) completeness (presence of all structure sections), and (4) description length (detail sufficiency). We validate our criterion on a manually annotated dataset. Experiments demonstrate CIDRe's superiority over existing metrics, achieving improvement in cross-entropy evaluation. When applied to filter comments, the models finetuned on CIDRe-filtered data show statistically significant quality gains in GPT-4o-mini assessments.

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Maria Dziuba, Valentin Malykh. 2025-05-26. CIDRe: A Reference-Free Multi-Aspect Criterion for Code Comment Quality Measurement. https://arxiv.org/abs/2505.19757

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