mlsanityData to 7 Oct 2026

A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes

arXiv 1704.04856 ↗First posted 17 Apr 2017cs.CLcited by 23

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Abstract (arXiv)

We propose a model to automatically describe changes introduced in the source code of a program using natural language. Our method receives as input a set of code commits, which contains both the modifications and message introduced by an user. These two modalities are used to train an encoder-decoder architecture. We evaluated our approach on twelve real world open source projects from four different programming languages. Quantitative and qualitative results showed that the proposed approach can generate feasible and semantically sound descriptions not only in standard in-project settings, but also in a cross-project setting.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Apr 2017.

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Citing papers per year: 2021: 7; 2022: 4; 2023: 4; 2024: 4; 2025: 4.

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  1. 2022Learning to Model Editing Processescited by 30
  2. 2023Distilled GPT for Source Code Summarizationcited by 30
  3. 2022RACE: Retrieval-Augmented Commit Message Generationcited by 27
  4. 2021Project-Level Encoding for Neural Source Code Summarization of Subroutinescited by 24
  5. 2021On the Evaluation of Commit Message Generation Models: An Experimental Studycited by 20
  6. 2021CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Modelcited by 16
  7. 2023From Commit Message Generation to History-Aware Commit Message Completioncited by 16
  8. 2022Source Code Summarization with Structural Relative Position Guided Transformercited by 14
  9. 2025From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligencecited by 13
  10. 2024KADEL: Knowledge-Aware Denoising Learning for Commit Message Generationcited by 10
  11. 2025Towards an Understanding of Context Utilization in Code Intelligencecited by 10
  12. 2024CommitBench: A Benchmark for Commit Message Generationcited by 8
11 more
  1. 2023Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarizationcited by 6
  2. 2021Learning to Describe Solutions for Bug Reports Based on Developer Discussionscited by 5
  3. 2022A Survey on Natural Language Processing for Programmingcited by 3
  4. 2024COMET: Generating Commit Messages using Delta Graph Context Representationcited by 3
  5. 2021Jointly Learning to Repair Code and Generate Commit Messagecited by 2
  6. 2023Statement-based Memory for Neural Source Code Summarizationcited by 2
  7. 2021Assessing the Effectiveness of Syntactic Structure to Learn Code Edit Representationscited by 1
  8. 2024Edisum: Summarizing and Explaining Wikipedia Edits at Scalecited by 1
  9. 2025CodeFuse-CommitEval: Towards Benchmarking LLM's Power on Commit Message and Code Change Inconsistency Detectioncited by 1
  10. 2021Borrowing from Similar Code: A Deep Learning NLP-Based Approach for Log Statement Automationcited by 0
  11. 2025CoRaCMG: Contextual Retrieval-Augmented Framework for Commit Message Generationcited by 0