中文标题#
後處理的 LLM 支持的分佈式進程調試
英文标题#
Post-hoc LLM-Supported Debugging of Distributed Processes
中文摘要#
在本文中，我們解決了人工調試的問題，這個問題目前仍然耗費資源且在某些方面顯得過時。 這一問題在日益複雜和分佈式的軟體系統中尤為明顯。 因此，我們這項工作的目標是介紹一種方法，該方法可以應用於任何系統，在宏觀和微觀層面都能簡化這一調試過程。 該方法利用系統的進程數據，並結合生成式人工智慧，生成自然語言解釋。 這些解釋是從實際的進程數據、介面信息和文檔中生成的，以更高效地引導開發人員理解進程及其子進程的行為和可能的錯誤。 在此，我們展示了一個演示程序，該程序在一個基於組件的 Java 系統上應用了這種方法。 然而，我們的方法與編程語言無關。 理想情況下，生成的解釋即使開發人員不熟悉所考慮系統的所有細節，也能提供對進程的良好理解。 我們的演示程序作為一個開源的 Web 應用程序提供，所有用戶都可以免費訪問。
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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