@inproceedings{mondal-etal-2023-robust,
title = "Robust Code Summarization",
author = "Mondal, Debanjan and
Lodha, Abhilasha and
Sahoo, Ankita and
Kumari, Beena",
editor = "Hupkes, Dieuwke and
Dankers, Verna and
Batsuren, Khuyagbaatar and
Sinha, Koustuv and
Kazemnejad, Amirhossein and
Christodoulopoulos, Christos and
Cotterell, Ryan and
Bruni, Elia",
booktitle = "Proceedings of the 1st GenBench Workshop on (Benchmarking) Generalisation in NLP",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.genbench-1.5",
doi = "10.18653/v1/2023.genbench-1.5",
pages = "65--75",
abstract = "This paper delves into the intricacies of code summarization using advanced transformer-based language models. Through empirical studies, we evaluate the efficacy of code summarization by altering function and variable names to explore whether models truly understand code semantics or merely rely on textual cues. We have also introduced adversaries like dead code and commented code across three programming languages (Python, Javascript, and Java) to further scrutinize the model{'}s understanding. Ultimately, our research aims to offer valuable insights into the inner workings of transformer-based LMs, enhancing their ability to understand code and contributing to more efficient software development practices and maintenance workflows.",
}
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%0 Conference Proceedings
%T Robust Code Summarization
%A Mondal, Debanjan
%A Lodha, Abhilasha
%A Sahoo, Ankita
%A Kumari, Beena
%Y Hupkes, Dieuwke
%Y Dankers, Verna
%Y Batsuren, Khuyagbaatar
%Y Sinha, Koustuv
%Y Kazemnejad, Amirhossein
%Y Christodoulopoulos, Christos
%Y Cotterell, Ryan
%Y Bruni, Elia
%S Proceedings of the 1st GenBench Workshop on (Benchmarking) Generalisation in NLP
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F mondal-etal-2023-robust
%X This paper delves into the intricacies of code summarization using advanced transformer-based language models. Through empirical studies, we evaluate the efficacy of code summarization by altering function and variable names to explore whether models truly understand code semantics or merely rely on textual cues. We have also introduced adversaries like dead code and commented code across three programming languages (Python, Javascript, and Java) to further scrutinize the model’s understanding. Ultimately, our research aims to offer valuable insights into the inner workings of transformer-based LMs, enhancing their ability to understand code and contributing to more efficient software development practices and maintenance workflows.
%R 10.18653/v1/2023.genbench-1.5
%U https://aclanthology.org/2023.genbench-1.5
%U https://doi.org/10.18653/v1/2023.genbench-1.5
%P 65-75
Markdown (Informal)
[Robust Code Summarization](https://aclanthology.org/2023.genbench-1.5) (Mondal et al., GenBench-WS 2023)
ACL
- Debanjan Mondal, Abhilasha Lodha, Ankita Sahoo, and Beena Kumari. 2023. Robust Code Summarization. In Proceedings of the 1st GenBench Workshop on (Benchmarking) Generalisation in NLP, pages 65–75, Singapore. Association for Computational Linguistics.