Automated Model Quality Estimation and Change Impact Analysis on Model Histories

Zusammenfassung

The development of Cyber-Physical Systems combines hardware with software in complex applications. To mitigate the complexity, collaborating system engineers rely on model-based approaches in systems engineering. Updates and function enhancements lead to frequent changing constraints and objectives. These changes increase the need to rework and extend model artifacts of the system. This can cause quality degradation over time due to errors, knowledge disparities or a lack of guidelines. To enable efficient collaboration and reduce maintenance costs in model-based systems engineering, industry needs a cost-efficient, scalable approach to monitor, and control model quality. The work outlines a doctoral thesis investigating the potential of automated data-driven quality assessment strategies using model artifact history and model changes. We will extract metrics and model changes to establish a quality feedback loop for system engineers. We aim to leverage the results of manual model quality assessments to incorporate domain-specific expert knowledge into the automated strategy. The main goal is to lower the effort of model quality assessments and provide practitioners with foresight on quality development and estimate task effort to improve model artifact quality.