DocGen: Generating Detailed Parameter Docstrings in Python
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Abstract (arXiv)
Documentation debt hinders the effective utilization of open-source software. Although code summarization tools have been helpful for developers, most would prefer a detailed account of each parameter in a function rather than a high-level summary. However, generating such a summary is too intricate for a single generative model to produce reliably due to the lack of high-quality training data. Thus, we propose a multi-step approach that combines multiple task-specific models, each adept at producing a specific section of a docstring. The combination of these models ensures the inclusion of each section in the final docstring. We compared the results from our approach with existing generative models using both automatic metrics and a human-centred evaluation with 17 participating developers, which proves the superiority of our approach over existing methods.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 63 references that are in the map. First posted on arXiv 11 Nov 2023.
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References · 22
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
- 2018CBAM: Convolutional Block Attention Modulecited by 2,477
- 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,310
- 2021CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generationcited by 906
- 2020GraphCodeBERT: Pre-training Code Representations with Data Flowcited by 609
- 2022UniXcoder: Unified Cross-Modal Pre-training for Code Representationcited by 361
- 2022Generating Training Data with Language Models: Towards Zero-Shot Language Understandingcited by 199
- 2018code2seq: Generating Sequences from Structured Representations of Codecited by 179
- 2020PyMT5: multi-mode translation of natural language and Python code with transformerscited by 80
- 2019A Neural Model for Generating Natural Language Summaries of Program Subroutinescited by 77
- 2021CoTexT: Multi-task Learning with Code-Text Transformercited by 61
- 2017A parallel corpus of Python functions and documentation strings for automated code documentation and code generationcited by 57
10 more
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- 2019TypeWriter: Neural Type Prediction with Search-based Validationcited by 32
- 2021ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generationcited by 31
- 2023An Empirical Comparison of Pre-Trained Models of Source Codecited by 25
- 2022Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Codecited by 21
- 2021Type4Py: Practical Deep Similarity Learning-Based Type Inference for Pythoncited by 19
- 2021Static Inference Meets Deep Learning: A Hybrid Type Inference Approach for Pythoncited by 12
- 2022A Large-Scale Comparison of Python Code in Jupyter Notebooks and Scriptscited by 7
- 2021What Do Developers Discuss about Code Comments?cited by 3
- 2021Do Comments follow Commenting Conventions? A Case Study in Java and Pythoncited by 2
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