基于GPT-4的代码仓库分析聊天机器人
A Tool-Augmented, GPT-4 Chatbot for Real-Time Repository Data Analysis
这个GPT-4聊天机器人能让非技术人员轻松理解代码仓库数据，通过工具选择和提示工程提高分析准确性。
研究人员推出一种新型聊天机器人架构，利用OpenAI的GPT-4模型自动提取和分析代码仓库数据。该架构通过解析用户查询提取相关参数，选择正确工具，然后调用GPT-4生成详细响应。实验在Issues、Pull Requests、Commits等多种问题类型上验证了该方法的有效性。
A Tool-Augmented, GPT-4 Chatbot for Real-Time Repository Data Analysis
Software repositories contain vast amounts of data on code contributions, bug reports, and project activities, yet this information remains challenging for non-technical stakeholders and developers to access due to limited expertise in querying repositories. To address this, we introduce a novel chatbot architecture leveraging OpenAI's GPT-4 model for automated extraction and analysis of repository data. In contrast, our architecture takes a structured path first by parsing the user's query to extract relevant parameters, then selecting the correct tool to employ based on that analysis, and finally invoking the GPT-4 model to create a highly detailed response. In contrast to previous work based on multi-component systems with embedding models and document retrievers, our architecture inverts the process by relying on prompt engineering and tool selection to fit with the query intent. To validate our approach, we conducted experiments on various question types, including Issues, Pull Requests, Commits, Compound Questions, and General Repository Information, evaluating our target prompts' ability to improve the accuracy of responses from the model. Beyond demonstrating the utility of this architecture to a diverse set of users, our findings suggest that this architecture can make repository data more accessible to technical and non-technical audiences through the production of actionable insights.
