Modeling the composition of analysis components and automatic constraint checking for semantic soundness

Component-based software architecture enables software architects to design complex systems by composing components that interact through well-defined, syntactically specified interfaces. A special kind of component we investigated in our previous work is the analysis components. Analysis components support the evaluation and prediction of system’s functional and non-functional properties. Evaluating these properties early in the development process helps optimize system performance and ensure compliance with requirements. While approaches for modeling and analyzing such systems, such as the Palladio approach, support syntactic validation of the composition, they often lack mechanisms to ensure the semantic soundness of compositions. In this paper, we present a model transformation approach to help architects ensure that system models are semantically sound and behave as expected. This approach enables the transformation of Palladio models into MontiArc models, allowing architects to enrich their system representations with semantic constraints and validate these constraints with the MontiArc workbench. This ensures that component interactions are consistent with both structural composition and intended semantics. We evaluate our approach through two different case studies. From these case studies, we derived several scenarios with varying constraints and states to assess the accuracy and performance of our approach. To evaluate accuracy, we examined our approach’s ability to check semantic constraints and detect violations. We observed high accuracy across the case studies. For performance, we analyze time complexity in different constraint types. The approach performed well when applied to arithmetic constraints, with its effectiveness decreasing when applied to more complex string-centered constraints. • Propose an approach to enrich Palladio models by specifying semantic constraints. • Develop a transformation process to convert Palladio models into MontiArc models for further analysis. • Introduce an approach to verify semantic constraints within MontiArc models. • Conduct case studies and systematically derive relevant scenarios to validate the proposed approach. • Evaluate the approach in terms of accuracy and performance.

Authors

Institutions

Publication Details

Journal
Journal of Systems and Software
Published
2025-09-27
DOI
https://doi.org/10.1016/j.jss.2025.112637
Primary Topic
Model-Driven Software Engineering Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Modeling the composition of analysis components and automatic constraint checking for semantic soundness

Jörg Henß, Bernhard Rumpe⋆, Sebastian Stüber, Robert Heinrich et al.
Journal of Systems and Software
Model-Driven Software Engineering Techniques
article

Modeling the composition of analysis components and automatic constraint checking for semantic soundness

Jörg Henß, Bernhard Rumpe⋆, Sebastian Stüber, Robert Heinrich, Thomas Weber, Sebastian Weber, Adrian Marin, Bahareh Taghavi
article en

Abstract

Component-based software architecture enables software architects to design complex systems by composing components that interact through well-defined, syntactically specified interfaces. A special kind of component we investigated in our previous work is the analysis components. Analysis components support the evaluation and prediction of system’s functional and non-functional properties. Evaluating these properties early in the development process helps optimize system performance and ensure compliance with requirements. While approaches for modeling and analyzing such systems, such as the Palladio approach, support syntactic validation of the composition, they often lack mechanisms to ensure the semantic soundness of compositions. In this paper, we present a model transformation approach to help architects ensure that system models are semantically sound and behave as expected. This approach enables the transformation of Palladio models into MontiArc models, allowing architects to enrich their system representations with semantic constraints and validate these constraints with the MontiArc workbench. This ensures that component interactions are consistent with both structural composition and intended semantics. We evaluate our approach through two different case studies. From these case studies, we derived several scenarios with varying constraints and states to assess the accuracy and performance of our approach. To evaluate accuracy, we examined our approach’s ability to check semantic constraints and detect violations. We observed high accuracy across the case studies. For performance, we analyze time complexity in different constraint types. The approach performed well when applied to arithmetic constraints, with its effectiveness decreasing when applied to more complex string-centered constraints. • Propose an approach to enrich Palladio models by specifying semantic constraints. • Develop a transformation process to convert Palladio models into MontiArc models for further analysis. • Introduce an approach to verify semantic constraints within MontiArc models. • Conduct case studies and systematically derive relevant scenarios to validate the proposed approach. • Evaluate the approach in terms of accuracy and performance.

Journal of Systems and SoftwareVol. 231
Karlsruhe Institute of Technology (DE), FZI Research Center for Information Technology (DE), Universität Ulm (DE), RWTH Aachen University (DE)
Openalex Percentile: Top 73%
Model-Driven Software Engineering Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.