用生成式AI自动解释分布式系统日志，帮开发者快速定位错误。
Post-hoc LLM-Supported Debugging of Distributed Processes
- 利用系统运行数据与AI生成自然语言说明
- 支持跨语言、跨系统，适用于宏观与微观调试
- 开源网页工具，无需熟悉系统细节也能理解错误
本文针对当前手动调试资源消耗大、效率低的问题，提出一种可应用于任意系统的后处理调试方法。该方法结合系统运行过程数据、接口信息与文档，通过生成式AI自动生成自然语言解释，帮助开发者更高效理解进程及其子进程的行为与潜在错误。我们构建了一个基于组件化Java系统的演示原型，该方法具有语言无关性，即使开发人员不熟悉系统全部细节，也能获得清晰的流程理解。演示系统以开源网页应用形式开放，供所有用户免费使用。
原文摘要 · Abstract (English)
In this paper, we address the problem of manual debugging, which nowadays remains resource-intensive and in some parts archaic. This problem is especially evident in increasingly complex and distributed software systems. Therefore, our objective of this work is to introduce an approach that can possibly be applied to any system, at both the macro- and micro-level, to ease this debugging process. This approach utilizes a system's process data, in conjunction with generative AI, to generate natural-language explanations. These explanations are generated from the actual process data, interface information, and documentation to guide the developers more efficiently to understand the behavior and possible errors of a process and its sub-processes. Here, we present a demonstrator that employs this approach on a component-based Java system. However, our approach is language-agnostic. Ideally, the generated explanations will provide a good understanding of the process, even if developers are not familiar with all the details of the considered system. Our demonstrator is provided as an open-source web application that is freely accessible to all users.
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