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Abstract <p>The problem of classifying source code reviews is considered. A new dataset 10 045 comments in size has been created and made publicly available, which combines four open datasets and is supplemented by 3200 hand-tagged comments. A hierarchical classification scheme has been developed, including 16 classes grouped into 5 categories. Various classification methods were tested, including FastText with F1-macro = 0.579 and CodeBERT with F1-macro = 0.775, with an assessment by computational costs. The obtained results confirm the applicability of automatic review classification for identifying discussions that require increased attention from developers.</p>

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Building a Dataset for Combined Classification of Source Code Reviews

  • 期刊论文

  • 发表日期：

    2025年9月1日

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  • P. A. Petrova,
  • S. I. Markov,
  • V. V. Kachanov

摘要

Abstract

The problem of classifying source code reviews is considered. A new dataset 10 045 comments in size has been created and made publicly available, which combines four open datasets and is supplemented by 3200 hand-tagged comments. A hierarchical classification scheme has been developed, including 16 classes grouped into 5 categories. Various classification methods were tested, including FastText with F1-macro = 0.579 and CodeBERT with F1-macro = 0.775, with an assessment by computational costs. The obtained results confirm the applicability of automatic review classification for identifying discussions that require increased attention from developers.