中文标题#
后处理的 LLM 支持的分布式进程调试
英文标题#
Post-hoc LLM-Supported Debugging of Distributed Processes
中文摘要#
在本文中，我们解决了人工调试的问题，这个问题目前仍然耗费资源且在某些方面显得过时。 这一问题在日益复杂和分布式的软件系统中尤为明显。 因此，我们这项工作的目标是介绍一种方法，该方法可以应用于任何系统，在宏观和微观层面都能简化这一调试过程。 该方法利用系统的进程数据，并结合生成式人工智能，生成自然语言解释。 这些解释是从实际的进程数据、接口信息和文档中生成的，以更高效地引导开发人员理解进程及其子进程的行为和可能的错误。 在此，我们展示了一个演示程序，该程序在一个基于组件的 Java 系统上应用了这种方法。 然而，我们的方法与编程语言无关。 理想情况下，生成的解释即使开发人员不熟悉所考虑系统的所有细节，也能提供对进程的良好理解。 我们的演示程序作为一个开源的 Web 应用程序提供，所有用户都可以免费访问。
英文摘要#
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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