Retrieval based code summarisation using code pre-trained models - Gupta, Sahaj (Nanyang Technological University, 2024)
Automatic code summarization has emerged as a valuable tool for enhancing software development speed and comprehension. In the context of source code summarization, where the goal is to generate concise and meaningful natural language summaries for given code snippets, pre-trained models have shown significant promise. This task involves understanding the semantics of code and generating human-readable descriptions, making it particularly challenging. Attention mechanisms, inherent in many pre-trained models, have shown to be particularly useful for source code summarization. They allow models to focus on relevant parts of the code when generating summaries, improving the coherence and informativeness of the generated text.