Debugging Tabular Log as Dynamic Graphs

• URL: http://arxiv.org/abs/2512.22903v1
• Date: Sun, 28 Dec 2025 12:23:41 GMT
• Title: Debugging Tabular Log as Dynamic Graphs
• Abstract summary: Tabular log abstracts objects and events in the real-world system and reports their updates to reflect the change of the system.<n>This paper proposes a new framework, GraphLog Debugger, to debug tabular log based on dynamic graphs.
• Score: 24.529035679332605
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tabular log abstracts objects and events in the real-world system and reports their updates to reflect the change of the system, where one can detect real-world inconsistencies efficiently by debugging corresponding log entries. However, recent advances in processing text-enriched tabular log data overly depend on large language models (LLMs) and other heavy-load models, thus suffering from limited flexibility and scalability. This paper proposes a new framework, GraphLogDebugger, to debug tabular log based on dynamic graphs. By constructing heterogeneous nodes for objects and events and connecting node-wise edges, the framework recovers the system behind the tabular log as an evolving dynamic graph. With the help of our dynamic graph modeling, a simple dynamic Graph Neural Network (GNN) is representative enough to outperform LLMs in debugging tabular log, which is validated by experimental results on real-world log datasets of computer systems and academic papers.

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