Post-hoc LLM-Supported Debugging of Distributed Processes
This addresses debugging inefficiencies for developers in distributed systems, though it appears incremental as it builds on existing AI and process analysis methods.
The paper tackles the problem of manual debugging in complex distributed software systems by introducing an approach that uses process data and generative AI to generate natural-language explanations, resulting in a demonstrator applied to a Java system that is language-agnostic and open-source.
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.