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\n  \n 2025\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Automated and Context-Aware Code Documentation Leveraging Advanced LLMs.\n \n \n \n \n\n\n \n Sarker, S. S.; and Ifty, T. T.\n\n\n \n\n\n\n In Proceedings of the 18th International Natural Language Generation Conference, pages 486–498, 2025. \n \n\n\n\n
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@inproceedings{sarker2025automated,\n  title={Automated and Context-Aware Code Documentation Leveraging Advanced LLMs},\n  author={Sarker, Swapnil Sharma and Ifty, Tanzina Taher},\n  booktitle={Proceedings of the 18th International Natural Language Generation Conference},\n  pages={486--498},\n  year={2025},\n  abstract= {Code documentation is essential to improve\nsoftware maintainability and comprehension.\nThe tedious nature of manual code documentation has led to much research on automated\ndocumentation generation. Existing automated\napproaches primarily focused on code summarization, leaving a gap in template-based documentation generation (e.g., Javadoc), particularly with publicly available Large Language\nModels (LLMs). Furthermore, progress in this\narea has been hindered by the lack of a Javadocspecific dataset that incorporates modern language features, provides broad framework/library coverage, and includes necessary contextual information. This study aims to address\nthese gaps by developing a tailored dataset\nand assessing the capabilities of publicly available LLMs for context-aware, template-based\nJavadoc generation. In this work, we present a\nnovel, context-aware dataset for Javadoc generation that includes critical structural and semantic information from modern Java codebases.\nWe evaluate five open-source LLMs (including\nLLaMA-3.1, Gemma-2, Phi-3, Mistral, Qwen2.5) using zero-shot, few-shot, and fine-tuned\nsetups and provide a comparative analysis of\ntheir performance. Our results demonstrate that\nLLaMA 3.1 performs consistently well and is\na reliable candidate for practical, automated\nJavadoc generation, offering a viable alternative to proprietary systems.},\nurl_paper= {https://aclanthology.org/2025.inlg-main.29.pdf},\n  link={https://scholar.google.com/scholar?hl=en&as_sdt=0%2C47&q=Automated+and+Context-Aware+Code+Documentation+Leveraging+Advanced+LLMs&btnG=}\n}\n
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\n Code documentation is essential to improve software maintainability and comprehension. The tedious nature of manual code documentation has led to much research on automated documentation generation. Existing automated approaches primarily focused on code summarization, leaving a gap in template-based documentation generation (e.g., Javadoc), particularly with publicly available Large Language Models (LLMs). Furthermore, progress in this area has been hindered by the lack of a Javadocspecific dataset that incorporates modern language features, provides broad framework/library coverage, and includes necessary contextual information. This study aims to address these gaps by developing a tailored dataset and assessing the capabilities of publicly available LLMs for context-aware, template-based Javadoc generation. In this work, we present a novel, context-aware dataset for Javadoc generation that includes critical structural and semantic information from modern Java codebases. We evaluate five open-source LLMs (including LLaMA-3.1, Gemma-2, Phi-3, Mistral, Qwen2.5) using zero-shot, few-shot, and fine-tuned setups and provide a comparative analysis of their performance. Our results demonstrate that LLaMA 3.1 performs consistently well and is a reliable candidate for practical, automated Javadoc generation, offering a viable alternative to proprietary systems.\n
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\n  \n 2024\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n Explainable lung disease classification from chest x-ray images utilizing deep learning and xai.\n \n \n \n\n\n \n Ifty, T. T.; Shafin, S. A.; Shahriar, S. M.; and Towhid, T.\n\n\n \n\n\n\n In 2024 IEEE 3rd International Conference on Computing and Machine Intelligence (ICMI), pages 1–5, 2024. IEEE\n \n\n\n\n
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@inproceedings{ifty2024explainable,\n  title={Explainable lung disease classification from chest x-ray images utilizing deep learning and xai},\n  author={Ifty, Tanzina Taher and Shafin, Saleh Ahmed and Shahriar, Shoeb Mohammad and Towhid, Tashfia},\n  booktitle={2024 IEEE 3rd International Conference on Computing and Machine Intelligence (ICMI)},\n  pages={1--5},\n  year={2024},\n  organization={IEEE}\n  }
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