发表机构
Islamic University of Technology; University of Dhaka（伊斯兰科技大学; 达卡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种基于GPT-4的工具增强型聊天机器人架构，通过查询解析、工具选择和提示工程，使软件仓库数据对技术和非技术用户更易访问，实验验证了其提升响应准确性的效果。
AI 中文摘要
软件仓库包含大量关于代码贡献、缺陷报告和项目活动的数据，然而，由于非技术利益相关者和开发者在查询仓库方面的专业知识有限，这些信息对他们而言仍然难以获取。为了解决这一问题，我们提出了一种新颖的聊天机器人架构，利用OpenAI的GPT-4模型来自动提取和分析仓库数据。相比之下，我们的架构首先采取结构化路径，即先解析用户的查询以提取相关参数，然后基于该分析选择要使用的正确工具，最后调用GPT-4模型来生成高度详细的响应。与以往基于包含嵌入模型和文档检索器的多组件系统的工作不同，我们的架构通过依赖提示工程和工具选择来匹配查询意图，从而反转了这一过程。为了验证我们的方法，我们针对多种问题类型进行了实验，包括问题（Issues）、拉取请求（Pull Requests）、提交（Commits）、复合问题（Compound Questions）和一般仓库信息（General Repository Information），评估了我们的目标提示在提高模型响应准确性方面的能力。除了证明该架构对多样化用户群体的实用性外，我们的研究结果表明，这种架构通过生成可操作的见解，可以使仓库数据对技术性和非技术性受众都更加易于访问。
英文摘要
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.
