@inproceedings{mahmud-etal-2021-code,
title = "Code to Comment Translation: A Comparative Study on Model Effectiveness {\&} Errors",
author = "Mahmud, Junayed and
Faisal, Fahim and
Arnob, Raihan Islam and
Anastasopoulos, Antonios and
Moran, Kevin",
editor = "Lachmy, Royi and
Yao, Ziyu and
Durrett, Greg and
Gligoric, Milos and
Li, Junyi Jessy and
Mooney, Ray and
Neubig, Graham and
Su, Yu and
Sun, Huan and
Tsarfaty, Reut",
booktitle = "Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nlp4prog-1.1",
doi = "10.18653/v1/2021.nlp4prog-1.1",
pages = "1--16",
abstract = "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 error taxonomy that can be used to drive future research efforts.",
}
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%0 Conference Proceedings
%T Code to Comment Translation: A Comparative Study on Model Effectiveness & Errors
%A Mahmud, Junayed
%A Faisal, Fahim
%A Arnob, Raihan Islam
%A Anastasopoulos, Antonios
%A Moran, Kevin
%Y Lachmy, Royi
%Y Yao, Ziyu
%Y Durrett, Greg
%Y Gligoric, Milos
%Y Li, Junyi Jessy
%Y Mooney, Ray
%Y Neubig, Graham
%Y Su, Yu
%Y Sun, Huan
%Y Tsarfaty, Reut
%S Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F mahmud-etal-2021-code
%X 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 error taxonomy that can be used to drive future research efforts.
%R 10.18653/v1/2021.nlp4prog-1.1
%U https://aclanthology.org/2021.nlp4prog-1.1
%U https://doi.org/10.18653/v1/2021.nlp4prog-1.1
%P 1-16
Markdown (Informal)
[Code to Comment Translation: A Comparative Study on Model Effectiveness & Errors](https://aclanthology.org/2021.nlp4prog-1.1) (Mahmud et al., NLP4Prog 2021)
ACL