一句话：首次系统评估扩散模型用于重建不完整执行轨迹事件序列，其中 SSSDS4 在多场景下表现最佳。
执行轨迹是理解系统与软件行为的关键手段，但轨迹事件的丢失会严重破坏数据完整性与后续分析。已有轨迹重建方法往往未能充分利用可用数据，在复杂、高维场景下效果受限。本文首次对扩散模型在重建不完整轨迹事件序列上的能力进行系统评估：作者基于 Phoronix Test Suite 生成九个不同数据集，并在多种序列长度与缺失比例下测试多个扩散模型。实验以准确率、perfect rate 与 ROUGE-L 为指标，结果表明 SSSDS4 模型在各类缺失填补场景中均取得最优性能。该工作说明基于扩散的生成模型能够较准确地还原缺失事件，从而有助于维持轨迹数据完整性并提升系统监控与分析质量。
Execution tracing is essential for understanding system and software behaviour, yet lost trace events can significantly compromise data integrity and analysis. Existing solutions for trace reconstruction often fail to fully leverage available data, particularly in complex and high-dimensional contexts. Recent advancements in generative artificial intelligence, particularly diffusion models, have set new benchmarks in image, audio, and natural language generation. This study conducts the first comprehensive evaluation of diffusion models for reconstructing incomplete trace event sequences. Using nine distinct datasets generated from the Phoronix Test Suite, we rigorously test these models on sequences of varying lengths and missing data ratios. Our results indicate that the SSSDS4model, in particular, achieves superior performance, in terms of accuracy, perfect rate, and ROUGE-L score across diverse imputation scenarios. These findings underscore the potential of diffusion-based models to accurately reconstruct missing events, thereby maintaining data integrity and enhancing system monitoring and analysis.
DOI 原文 ·
@inproceedings{paperbot8843,
title = {Execution Trace Reconstruction Using Diffusion-Based Generative Models},
author = {Madeline Janecek and Naser Ezzati-Jivan and Abdelwahab Hamou-Lhadj},
booktitle = {ICSE},
year = {2025},
doi = {10.1109/icse55347.2025.00063}
}