# Code to Comment Translation: A Comparative Study on Model Effectiveness & Errors

arXiv: https://arxiv.org/abs/2106.08415 · HTML: https://mlsanity.com/paper/2106.08415/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 46 references that are in the map.
- First posted on arXiv 15 Jun 2021.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021)
- [CodeSearchNet Challenge: Evaluating the State of Semantic Code Search](https://mlsanity.com/paper/1909.09436/) (2019)
- [code2seq: Generating Sequences from Structured Representations of Code](https://mlsanity.com/paper/1808.01400/) (2018)
- [A Transformer-based Approach for Source Code Summarization](https://mlsanity.com/paper/2005.00653/) (2020)
- [Improving Automatic Source Code Summarization via Deep Reinforcement Learning](https://mlsanity.com/paper/1811.07234/) (2018)
- [Retrieval-Augmented Generation for Code Summarization via Hybrid GNN](https://mlsanity.com/paper/2006.05405/) (2020)
- [Improved Code Summarization via a Graph Neural Network](https://mlsanity.com/paper/2004.02843/) (2020)
- [A Neural Model for Generating Natural Language Summaries of Program Subroutines](https://mlsanity.com/paper/1902.01954/) (2019)
- [Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks](https://mlsanity.com/paper/2102.02017/) (2021)
- [Recommendations for Datasets for Source Code Summarization](https://mlsanity.com/paper/1904.02660/) (2019)
- [Code to Comment "Translation": Data, Metrics, Baselining & Evaluation](https://mlsanity.com/paper/2010.01410/) (2020)
- [Project-Level Encoding for Neural Source Code Summarization of Subroutines](https://mlsanity.com/paper/2103.11599/) (2021)
- [Automatic Code Summarization: A Systematic Literature Review](https://mlsanity.com/paper/1909.04352/) (2019)
- [Incorporating Structural Alignment Biases into an Attentional Neural Translation Model](https://mlsanity.com/paper/1601.01085/) (2016)
- [Enabling Mutation Testing for Android Apps](https://mlsanity.com/paper/1707.09038/) (2017)

## Built on by (most cited first, 6 of 6)

- [Multilingual training for Software Engineering](https://mlsanity.com/paper/2112.02043/) (2021)
- [On the Transferability of Pre-trained Language Models for Low-Resource Programming Languages](https://mlsanity.com/paper/2204.09653/) (2022)
- [The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation](https://mlsanity.com/paper/2305.06156/) (2023)
- [Source Code Summarization with Structural Relative Position Guided Transformer](https://mlsanity.com/paper/2202.06521/) (2022)
- [Large Language Models are Qualified Benchmark Builders: Rebuilding Pre-Training Datasets for Advancing Code Intelligence Tasks](https://mlsanity.com/paper/2504.19444/) (2025)
- [A Survey of Deep Learning Models for Structural Code Understanding](https://mlsanity.com/paper/2205.01293/) (2022)

## Abstract (arXiv)

Automated source code summarization is a popular software engineering research topic wherein machine translation models are employed to "translate" code snippets into relevant natural language descriptions. Most evaluations of such models are conducted using automatic reference-based metrics. However, given the relatively large semantic gap between programming languages and natural language, we argue that this line of research would benefit from a qualitative investigation into the various error modes of current state-of-the-art models. Therefore, in this work, we perform both a quantitative and qualitative comparison of three recently proposed source code summarization models. In our quantitative evaluation, we compare the models based on the smoothed BLEU-4, METEOR, and ROUGE-L machine translation metrics, and in our qualitative evaluation, we perform a manual open-coding of the most common errors committed by the models when compared to ground truth captions. Our investigation reveals new insights into the relationship between metric-based performance and model prediction errors grounded in an empirically derived error taxonomy that can be used to drive future research efforts