# BashExplainer: Retrieval-Augmented Bash Code Comment Generation based on Fine-tuned CodeBERT arXiv: https://arxiv.org/abs/2206.13325 ยท HTML: https://mlsanity.com/paper/2206.13325/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Placed in RAG for code, one of 15 directions in Retrieval-augmented generation. - Builds on 24 of its 56 references that are in the map. - First posted on arXiv 27 Jun 2022. ## Placed in - Retrieval-augmented generation / [RAG for code](https://mlsanity.com/rag/rag-for-code/) ## Builds on (most cited first, 24 of 24) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014) - [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014) - [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014) - [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020) - [Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models](https://mlsanity.com/paper/1610.02424/) (2016) - [Query-Key Normalization for Transformers](https://mlsanity.com/paper/2010.04245/) (2020) - [Beam Search Strategies for Neural Machine Translation](https://mlsanity.com/paper/1702.01806/) (2017) - [Incorporating Copying Mechanism in Sequence-to-Sequence Learning](https://mlsanity.com/paper/1603.06393/) (2016) - [A Transformer-based Approach for Source Code Summarization](https://mlsanity.com/paper/2005.00653/) (2020) - [Sequence-to-Sequence Learning as Beam-Search Optimization](https://mlsanity.com/paper/1606.02960/) (2016) - [Improving Automatic Source Code Summarization via Deep Reinforcement Learning](https://mlsanity.com/paper/1811.07234/) (2018) - [NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System](https://mlsanity.com/paper/1802.08979/) (2018) - [Retrieval-Augmented Generation for Code Summarization via Hybrid GNN](https://mlsanity.com/paper/2006.05405/) (2020) - [Retrieve and Refine: Exemplar-based Neural Comment Generation](https://mlsanity.com/paper/1910.10419/) (2019) - [Simple and Effective Text Matching with Richer Alignment Features](https://mlsanity.com/paper/1908.00300/) (2019) - [Shellcode_IA32: A Dataset for Automatic Shellcode Generation](https://mlsanity.com/paper/2104.13100/) (2021) - [Retrieve and Refine: Exemplar-based Neural Comment Generation](https://mlsanity.com/paper/2010.04459/) (2020) - [DualSC: Automatic Generation and Summarization of Shellcode via Transformer and Dual Learning](https://mlsanity.com/paper/2202.09785/) (2022) - [Fine-grained Pseudo-code Generation Method via Code Feature Extraction and Transformer](https://mlsanity.com/paper/2102.06360/) (2021) - [Automated Query Reformulation for Efficient Search based on Query Logs From Stack Overflow](https://mlsanity.com/paper/2102.00826/) (2021) - [Shell Language Processing: Unix command parsing for Machine Learning](https://mlsanity.com/paper/2107.02438/) (2021) - [SOTitle: A Transformer-based Post Title Generation Approach for Stack Overflow](https://mlsanity.com/paper/2202.09789/) (2022) - [ComFormer: Code Comment Generation via Transformer and Fusion Method-based Hybrid Code Representation](https://mlsanity.com/paper/2107.03644/) (2021) ## Built on by (most cited first, 9 of 9) - [Retrieval-Augmented Generation for AI-Generated Content: A Survey](https://mlsanity.com/paper/2402.19473/) (2024) - [A Survey on Knowledge-Oriented Retrieval-Augmented Generation](https://mlsanity.com/paper/2503.10677/) (2025) - [Who Evaluates the Evaluators? On Automatic Metrics for Assessing AI-based Offensive Code Generators](https://mlsanity.com/paper/2212.06008/) (2022) - [LevelRAG: Enhancing Retrieval-Augmented Generation with Multi-hop Logic Planning over Rewriting Augmented Searchers](https://mlsanity.com/paper/2502.18139/) (2025) - [On Codex Prompt Engineering for OCL Generation: An Empirical Study](https://mlsanity.com/paper/2303.16244/) (2023) - [Retrieval-Augmented Generation in Industry: An Interview Study on Use Cases, Requirements, Challenges, and Evaluation](https://mlsanity.com/paper/2508.14066/) (2025) - [Impact-driven Context Filtering For Cross-file Code Completion](https://mlsanity.com/paper/2508.05970/) (2025) - [Multi-Granularity Detector for Vulnerability Fixes](https://mlsanity.com/paper/2305.13884/) (2023) - [Enhancing Robustness of AI Offensive Code Generators via Data Augmentation](https://mlsanity.com/paper/2306.05079/) (2023) ## Abstract (arXiv) Developers use shell commands for many tasks, such as file system management, network control, and process management. Bash is one of the most commonly used shells and plays an important role in Linux system development and maintenance. Due to the language flexibility of Bash code, developers who are not familiar with Bash often have difficulty understanding the purpose and functionality of Bash code. In this study, we study Bash code comment generation problem and proposed an automatic method BashExplainer based on two-stage training strategy. In the first stage, we train a Bash encoder by fine-tuning CodeBERT on our constructed Bash code corpus. In the second stage, we first retrieve the most similar code from the code repository for the target code based on semantic and lexical similarity. Then we use the trained Bash encoder to generate two vector representations. Finally, we fuse these two vector representations via the fusion layer and generate the code comment through the decoder. To show the competitiveness of our proposed method, we construct a high-quality corpus by combining the corpus shared in the previous NL2Bash study and the corpus shared in the NLC2CMD competition. This corpus contains 10,592 Bash codes and corresponding comments. Then we selected ten baselines from previous studies on automatic code comment generation, which cover information retrieval methods, deep learning methods, and hybrid methods.