Summarize Me: The Future of Issue Thread Interpretation

Understanding issue threads is an essential aspect of software maintenance and development, aiding developers in effectively addressing and managing software-related issues. These threads typically contain an issue description, comments discussing possible solutions, and often culminate in a pull request where the proposed changes are elaborated. Even though they are crucial, understanding issue threads can be a lot of work because they are often long and complex, particularly in big projects. This paper, therefore, aims to automate the process of issue thread summarization using advanced AI models, specifically the GPT-3.5-Turbo, reducing the time spent and improving the efficiency of the interpretation process. Our approach taps into the potential of the zero-shot learning methodology, enabling the model to produce context-specific summaries without reliance on prior examples. Additionally, we have developed an algorithm that determines the most effective length for these summaries, which enhances their clarity and relevance. The performance of the model is assessed using automated metrics, including ROUGE and BART scores, for extractive and abstractive summary evaluation respectively. Further, we may like to add that summaries of around 30% to 40% of the total size of the issue thread appears to be sufficient, though it varies slightly from case to case. The model's successful generation of brief, clear, and pertinent summaries not only boosts team communication and project management but also lays the groundwork for its future integration into a comprehensive tool for simplified exploration and comprehension of complex software repositories.