TraceSynth: Generating Production-Quality Kernel Traces with Constraint-Guided Diffusion Models

Jul 15, 2026

TraceSynth is a diffusion-based framework for generating synthetic kernel execution traces to augment limited real data for machine learning-based system diagnostics. It models traces as multi-channel sequences and applies constraint-guided repair to enforce system invariants. Experiments show that, for deterministic workloads, synthetic augmentation achieves up to 87.2% F1-Macro, with context length and constraint-guided repair significantly impacting quality. The approach reduces data collection costs while maintaining high performance for downstream tasks.

Why it matters: This work enables cost-effective augmentation of kernel traces in production observability pipelines, addressing runtime overhead, storage, and privacy constraints in industrial systems.

Full story at: arXiv Software Engineering ↗