mlsanityData to 7 Oct 2026

Learning to Generate Code Comments from Class Hierarchies

arXiv 2103.13426 ↗First posted 24 Mar 2021cs.CL cs.LG cs.SEcited by 5

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Abstract (arXiv)

Descriptive code comments are essential for supporting code comprehension and maintenance. We propose the task of automatically generating comments for overriding methods. We formulate a novel framework which accommodates the unique contextual and linguistic reasoning that is required for performing this task. Our approach features: (1) incorporating context from the class hierarchy; (2) conditioning on learned, latent representations of specificity to generate comments that capture the more specialized behavior of the overriding method; and (3) unlikelihood training to discourage predictions which do not conform to invariant characteristics of the comment corresponding to the overridden method. Our experiments show that the proposed approach is able to generate comments for overriding methods of higher quality compared to prevailing comment generation techniques.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 20 of its 41 references that are in the map. First posted on arXiv 24 Mar 2021.

Builds on

20 of 41 references.

References · 20

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8 more
  1. 2020PyMT5: multi-mode translation of natural language and Python code with transformerscited by 80
  2. 2020Improved Code Summarization via a Graph Neural Networkcited by 79
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Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 1; 2025: 1.

5 papers.

  1. 2022Repository-Level Prompt Generation for Large Language Models of Codecited by 106
  2. 2024Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedbackcited by 45
  3. 2023RepoFusion: Training Code Models to Understand Your Repositorycited by 40
  4. 2022An Extractive-and-Abstractive Framework for Source Code Summarizationcited by 15
  5. 2025Towards an Understanding of Context Utilization in Code Intelligencecited by 10