Execution Trace Reconstruction Using Diffusion-Based Generative Models

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 SSSD<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> model, 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.