Compositionality in Model-Based Testing

TL;DR

This paper introduces a new relation called mutual acceptance that enables compositional model-based testing, allowing for scalable testing of large systems by composing smaller models while ensuring correct communication.

Contribution

The paper proposes mutual acceptance as a novel relation that guarantees communication correctness in composed models, addressing the non-compositionality of existing MBT conformance relations.

Findings

Mutual acceptance ensures correct communication between components.

It improves scalability in testing large systems.

Facilitates retesting and diagnosis of system components.

Abstract

Model-based testing (MBT) promises a scalable solution to testing large systems, if a model is available. Creating these models for large systems, however, has proven to be difficult. Composing larger models from smaller ones could solve this, but our current MBT conformance relation $\textbf{uioco}$ is not compositional, i.e. correctly tested components, when composed into a system, can still lead to a faulty system. To catch these integration problems, we introduce a new relation over component models called $\textbf{mutual acceptance}$. Mutually accepting components are guaranteed to communicate correctly, which makes MBT compositional. In addition to providing compositionality, mutual acceptance has benefits when retesting systems with updated components, and when diagnosing systems consisting of components.