A Survey of Automatic Generation of Source Code Comments: Algorithms and Techniques
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Abstract (arXiv)
As an integral part of source code files, code comments help improve program readability and comprehension. However, developers sometimes do not comment on their program code adequately due to the incurred extra efforts, lack of relevant knowledge, unawareness of the importance of code commenting or some other factors. As a result, code comments can be inadequate, absent or even mismatched with source code, which affects the understanding, reusing and the maintenance of software. To solve these problems of code comments, researchers have been concerned with generating code comments automatically. In this work, we aim at conducting a survey of automatic code commenting researches. First, we generally analyze the challenges and research framework of automatic generation of program comments. Second, we present the classification of representative algorithms, the design principles, strengths and weaknesses of each category of algorithms. Meanwhile, we also provide an overview of the quality assessment of the generated comments. Finally, we summarize some future directions for advancing the techniques of automatic generation of code comments and the quality assessment of comments.
Key facts. Cited by 15 papers in the map (data through 13 Oct 2026). First posted on arXiv 25 Jul 2019.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 6; 2025: 5; 2026: 1.
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- 2021On the Evaluation of Neural Code Summarizationcited by 48
- 2025Position: The ML Community Must Build an AI-Augmented Peer-Review Ecosystemcited by 8
- 2024Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generationcited by 7
- 2025Large Language Models are Qualified Benchmark Builders: Rebuilding Pre-Training Datasets for Advancing Code Intelligence Taskscited by 6
- 2023Semantic Similarity Loss for Neural Source Code Summarizationcited by 5
- 2022CodeExp: Explanatory Code Document Generationcited by 4
- 2024Renaissance of Literate Programming in the Era of LLMs: Enhancing LLM-Based Code Generation in Large-Scale Projectscited by 3
- 2025Commenting Higher-level Code Unit: Full Code, Reduced Code, or Hierarchical Code Summarizationcited by 3
- 2025Across Programming Language Silos: A Study on Cross-Lingual Retrieval-augmented Code Generationcited by 3
- 2024MESIA: Understanding and Leveraging Supplementary Nature of Method-level Comments for Automatic Comment Generationcited by 2
- 2024DeepCodeProbe: Towards Understanding What Models Trained on Code Learncited by 1
- 2024Multi-Representation Genetic Programming: A Case Study on Tree-based and Linear Representationscited by 0
- 2026Prompt-Driven Code Summarization: A Systematic Literature Review16 Apr 2026
- 2025Position: The ML Community Must Build an AI-Augmented Peer-Review Ecosystem9 Jun 2025
- 2025Across Programming Language Silos: A Study on Cross-Lingual Retrieval-augmented Code Generation4 Jun 2025
- 2025AI-Mediated Code Comment Improvement13 May 2025
- 2025Large Language Models are Qualified Benchmark Builders: Rebuilding Pre-Training Datasets for Advancing Code Intelligence Tasks28 Apr 2025
- 2025Commenting Higher-level Code Unit: Full Code, Reduced Code, or Hierarchical Code Summarization13 Mar 2025
- 2024Renaissance of Literate Programming in the Era of LLMs: Enhancing LLM-Based Code Generation in Large-Scale Projects25 Dec 2024
- 2024Strategic Optimization and Challenges of Large Language Models in Object-Oriented Programming27 Aug 2024
- 2024DeepCodeProbe: Towards Understanding What Models Trained on Code Learn11 Jul 2024
- 2024Multi-Representation Genetic Programming: A Case Study on Tree-based and Linear Representations23 May 2024
- 2024MESIA: Understanding and Leveraging Supplementary Nature of Method-level Comments for Automatic Comment Generation26 Mar 2024
- 2024Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generation5 Mar 2024
3 more
- 2023Semantic Similarity Loss for Neural Source Code Summarization14 Aug 2023
- 2022CodeExp: Explanatory Code Document Generation25 Nov 2022
- 2021On the Evaluation of Neural Code Summarization15 Jul 2021
- 2021On the Evaluation of Neural Code Summarizationcited by 48
- 2025Position: The ML Community Must Build an AI-Augmented Peer-Review Ecosystemcited by 8
- 2024Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generationcited by 7
- 2025Large Language Models are Qualified Benchmark Builders: Rebuilding Pre-Training Datasets for Advancing Code Intelligence Taskscited by 6
- 2023Semantic Similarity Loss for Neural Source Code Summarizationcited by 5
- 2022CodeExp: Explanatory Code Document Generationcited by 4
- 2024Renaissance of Literate Programming in the Era of LLMs: Enhancing LLM-Based Code Generation in Large-Scale Projectscited by 3
- 2025Commenting Higher-level Code Unit: Full Code, Reduced Code, or Hierarchical Code Summarizationcited by 3
- 2024MESIA: Understanding and Leveraging Supplementary Nature of Method-level Comments for Automatic Comment Generationcited by 2
- 2024DeepCodeProbe: Towards Understanding What Models Trained on Code Learncited by 1
- 2024Multi-Representation Genetic Programming: A Case Study on Tree-based and Linear Representationscited by 0
- 2024Strategic Optimization and Challenges of Large Language Models in Object-Oriented Programmingcited by 0
12 most cited of 14 All 14 (JSON)
- 2026Prompt-Driven Code Summarization: A Systematic Literature Review16 Apr 2026
- 2025Position: The ML Community Must Build an AI-Augmented Peer-Review Ecosystem9 Jun 2025
- 2025AI-Mediated Code Comment Improvement13 May 2025
- 2025Large Language Models are Qualified Benchmark Builders: Rebuilding Pre-Training Datasets for Advancing Code Intelligence Tasks28 Apr 2025
- 2025Commenting Higher-level Code Unit: Full Code, Reduced Code, or Hierarchical Code Summarization13 Mar 2025
- 2024Renaissance of Literate Programming in the Era of LLMs: Enhancing LLM-Based Code Generation in Large-Scale Projects25 Dec 2024
- 2024Strategic Optimization and Challenges of Large Language Models in Object-Oriented Programming27 Aug 2024
- 2024DeepCodeProbe: Towards Understanding What Models Trained on Code Learn11 Jul 2024
- 2024Multi-Representation Genetic Programming: A Case Study on Tree-based and Linear Representations23 May 2024
- 2024MESIA: Understanding and Leveraging Supplementary Nature of Method-level Comments for Automatic Comment Generation26 Mar 2024
- 2024Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generation5 Mar 2024
- 2023Semantic Similarity Loss for Neural Source Code Summarization14 Aug 2023
12 newest of 14 All 14 (JSON)