Automatic Code Comment Generator using T5-transformer

Author(s)	Mr. FARHAN MUHAMED C A, Prof. Vishnu Mohan C
Country	India
Abstract	Automatic code comment generation is an emerging area of research at the intersection of software engineering and natural language processing. This work presents the design and implementation of an AI-driven comment generation system fine-tuned on the CodeT5-base transformer architecture, tailored for Python code. The approach leverages recent advances in sequence-to-sequence modeling to produce concise, contextually relevant, and semantically accurate comments in real time. By addressing key challenges in preprocessing, training optimization, and model integration, the system achieves high-quality outputs with fast inference performance. This study demonstrates the potential of transformer-based models to enhance software doc umentation, support code comprehension, and improve developer productivity in modern software engineering workflows.
Keywords	Code Comment Generation, CodeT5, Trans former Models, Software Documentation, Natural Language Processing, Python.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 5, September-October 2025
Published On	2025-10-18
DOI	https://doi.org/10.36948/ijfmr.2025.v07i05.58231

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International Journal For Multidisciplinary Research