Automatic Reuse, Adaption, and Execution of Simulation Experiments via Provenance Patterns

TL;DR

This paper presents RASE, a framework that automatically reuses and adapts simulation experiments by analyzing provenance graphs, enabling more systematic and flexible experiment management across different scenarios.

Contribution

It introduces provenance patterns and transformation rules to automatically identify and adapt simulation experiments, advancing beyond user-dependent reuse methods.

Findings

Effective reuse of experiments in case studies

Supports multiple modeling tools

Automates adaptation of experiments

Abstract

Simulation experiments are typically conducted repeatedly during the model development process, for example, to re-validate if a behavioral property still holds after several model changes. Approaches for automatically reusing and generating simulation experiments can support modelers in conducting simulation studies in a more systematic and effective manner. They rely on explicit experiment specifications and, so far, on user interaction for initiating the reuse. Thereby, they are constrained to support the reuse of simulation experiments in a specific setting. Our approach now goes one step further by automatically identifying and adapting the experiments to be reused for a variety of scenarios. To achieve this, we exploit provenance graphs of simulation studies, which provide valuable information about the previous modeling and experimenting activities, and contain meta-information about the different entities that were used or produced during the simulation study. We define provenance patterns and associate them with a semantics, which allows us to interpret the different activities, and construct transformation rules for provenance graphs. Our approach is implemented in a Reuse and Adapt framework for Simulation Experiments (RASE) which can interface with various modeling and simulation tools. In the case studies, we demonstrate the utility of our framework for a) the repeated sensitivity analysis of an agent-based model of migration routes, and b) the cross-validation of two models of a cell signaling pathway.