Code Summarization and Awareness Framework for Repositories

In this article, we propose a novel framework for generating code summaries that can be used to improve the comprehension and readability of complex software systems. Our approach leverages the power of language models (LLMs) to generate concise and accurate summaries of functions, which can help developers quickly understand the purpose and functionality of a given function without having to read through lengthy code snippets.
The proposed framework consists of two main components: a prompt generation module and a language model (GPT-3.5-Turbo) based summary generator. The prompt generation module creates a detailed description of the function, including its parameters, return type, and examples of how it can be used. This description is then passed to the language model, which generates a concise summary of the function based on the provided information.
Our approach has several key advantages over traditional code summarization methods. Firstly, our framework can generate summaries for functions without any comments or documentation, making it particularly useful for functions that lack clear explanations. Secondly, our summaries are tailored to the specific function being analyzed, ensuring that they accurately convey the purpose and functionality of the code. Finally, our approach leverages the latest advances in LLM technology to generate high-quality summaries with minimal training data.
We evaluate our framework using a dataset of real-world functions and demonstrate its effectiveness in generating accurate and informative summaries. Our results show that our approach outperforms traditional code summarization methods, which often rely on manual inspection or automated techniques that lack contextual understanding.
In conclusion, our proposed framework for generating code summaries has the potential to significantly improve the comprehension and readability of complex software systems. By leveraging the power of LLMs, we can generate concise and accurate summaries of functions without requiring extensive training data or manual inspection. As software systems continue to grow in complexity, our approach could become an essential tool for developers seeking to quickly grasp the purpose and functionality of a given function.