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        <title>Read between the lines - Functionality Extraction From READMEs</title>
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    <abstract>While text summarization is a well-known NLP task, in this paper, we introduce a novel and useful variant of it called functionality extraction from Git README files. Though this task is a text2text generation at an abstract level, it involves its own peculiarities and challenges making existing text2text generation systems not very useful. The motivation behind this task stems from a recent surge in research and development activities around the use of large language models for code-related tasks, such as code refactoring, code summarization, etc. We also release a human-annotated dataset called FuncRead, and develop a battery of models for the task. Our exhaustive experimentation shows that small size fine-tuned models beat any baseline models that can be designed using popular black-box or white-box large language models (LLMs) such as ChatGPT and Bard. Our best fine-tuned 7 Billion CodeLlama model exhibit 70% and 20% gain on the F1 score against ChatGPT and Bard respectively.</abstract>
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%0 Conference Proceedings
%T Read between the lines - Functionality Extraction From READMEs
%A Kumar, Prince
%A Tamilselvam, Srikanth
%A Garg, Dinesh
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F kumar-etal-2024-read
%X While text summarization is a well-known NLP task, in this paper, we introduce a novel and useful variant of it called functionality extraction from Git README files. Though this task is a text2text generation at an abstract level, it involves its own peculiarities and challenges making existing text2text generation systems not very useful. The motivation behind this task stems from a recent surge in research and development activities around the use of large language models for code-related tasks, such as code refactoring, code summarization, etc. We also release a human-annotated dataset called FuncRead, and develop a battery of models for the task. Our exhaustive experimentation shows that small size fine-tuned models beat any baseline models that can be designed using popular black-box or white-box large language models (LLMs) such as ChatGPT and Bard. Our best fine-tuned 7 Billion CodeLlama model exhibit 70% and 20% gain on the F1 score against ChatGPT and Bard respectively.
%R 10.18653/v1/2024.findings-naacl.251
%U https://aclanthology.org/2024.findings-naacl.251/
%U https://doi.org/10.18653/v1/2024.findings-naacl.251
%P 3977-3990

Markdown (Informal)

[Read between the lines - Functionality Extraction From READMEs](https://aclanthology.org/2024.findings-naacl.251/) (Kumar et al., Findings 2024)

• Read between the lines - Functionality Extraction From READMEs (Kumar et al., Findings 2024)

ACL

• Prince Kumar, Srikanth Tamilselvam, and Dinesh Garg. 2024. Read between the lines - Functionality Extraction From READMEs. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 3977–3990, Mexico City, Mexico. Association for Computational Linguistics.