Automated Code Comment Generation System Using Transformer Models (BERT).

The primary aim of the project is to develop a deep learning-based solution that can automatically generate descriptive comments for source code in languages like Python or Java. Writing comments is a critical aspect of software engineering, yet it is often overlooked, resulting in poorly documented and harder-to-maintain code. This project aims to solve this problem by training a transformer-based model (BERT) on a large dataset of code-comment pairs. The model learns the relationship between code structures and natural language annotations, enabling it to generate accurate and informative comments for new, unseen code snippets. By the end of the project, students are expected to deliver a functioning model that can accept raw code input and output useful comments, enhancing software documentation and supporting developers in writing clean, understandable code.

The successful completion of this project will involve a sequence of defined activities over a twelve-week timeline. Initially, students will be introduced to the foundational concepts of transformer models and the BERT architecture. In the second week, they will begin gathering and preparing datasets containing code and corresponding comments from sources like GitHub and CodeSearchNet. Students will also explore essential machine learning libraries and tools, such as TensorFlow and Hugging Face’s Transformers, to build and fine-tune their models.

As the project progresses, they will design and implement a model framework to generate comments from code snippets. Training and evaluation of the model will follow, using quality metrics like BLEU or ROUGE to measure the effectiveness of the generated comments. Students will iteratively improve the model’s performance and, later in the project, develop a user interface that allows developers to input code and view generated comments in real-time. The final stages involve testing, comprehensive documentation, and a team presentation to demonstrate the final system. Throughout the project, students are expected to collaborate effectively, follow clean coding standards, and ensure their work is original and ethically sound.