TAAF: A Trace Abstraction and Analysis Framework Synergizing Knowledge Graphs and LLMs

• URL: http://arxiv.org/abs/2601.02632v1
• Date: Tue, 06 Jan 2026 01:04:05 GMT
• Title: TAAF: A Trace Abstraction and Analysis Framework Synergizing Knowledge Graphs and LLMs
• Authors: Alireza Ezaz, Ghazal Khodabandeh, Majid Babaei, Naser Ezzati-Jivan,
• Abstract summary: This paper introduces TAAF (Trace Abstraction and Analysis Framework), a novel approach to transform raw trace data into actionable insights.<n>An LLM interprets query-specific subgraphs to answer natural-language questions, reducing the need for manual inspection.<n>Experiments show that TAAF improves answer accuracy by up to 31.2%, particularly in multi-hop and causal reasoning tasks.
• Score: 3.2839783281320085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Execution traces are a critical source of information for understanding, debugging, and optimizing complex software systems. However, traces from OS kernels or large-scale applications like Chrome or MySQL are massive and difficult to analyze. Existing tools rely on predefined analyses, and custom insights often require writing domain-specific scripts, which is an error-prone and time-consuming task. This paper introduces TAAF (Trace Abstraction and Analysis Framework), a novel approach that combines time-indexing, knowledge graphs (KGs), and large language models (LLMs) to transform raw trace data into actionable insights. TAAF constructs a time-indexed KG from trace events to capture relationships among entities such as threads, CPUs, and system resources. An LLM then interprets query-specific subgraphs to answer natural-language questions, reducing the need for manual inspection and deep system expertise. To evaluate TAAF, we introduce TraceQA-100, a benchmark of 100 questions grounded in real kernel traces. Experiments across three LLMs and multiple temporal settings show that TAAF improves answer accuracy by up to 31.2%, particularly in multi-hop and causal reasoning tasks. We further analyze where graph-grounded reasoning helps and where limitations remain, offering a foundation for next-generation trace analysis tools.

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