Andreas Demuth

Cross-layer modeler: a tool for flexible multilevel modeling with consistency checking

Model-driven engineering has become a popular methodology in software engineering. Most available modeling tools support the creation of models based on a fixed metamodel. Typically, tool users cannot change the metamodel to reflect domain changes or newly emerged requirements. As a consequence, an updated version of the tool with an evolved metamodel must be developed and models as well as constraints that ensure model consistency have to be co-evolved, often manually, to conform to the new metamodel. Both, tool evolution and the necessary co-evolutions, are time consuming and error prone tasks. Furthermore, common tools often restrict the number of metalevels that can be modeled and force modelers to use workarounds to express certain facts. To overcome these issues we present the Cross-Layer Modeler (XLM), a modeling tool that supports multilevel modeling and allows co-evolution of metamodels and models. The XLM automatically performs co-evolution of constraints and gives instant feedback about model consistency. We illustrate the novel modeling approach of our tool and discuss its main capabilities.

Supporting the Co-evolution of Metamodels and Constraints through Incremental Constraint Management

Design models must abide by constraints that can come from diverse sources, like metamodels, requirements, or the problem domain. Modelers intent to live by these constraints and thus desire automated mechanism that provide instant feedback on constraint violations. However, typical approaches assume that constraints do not evolve over time, which, unfortunately, is becoming increasingly unrealistic. For example, the co-evolution of metamodels and models requires corresponding constraints to be co-evolved continuously. This demands efficient constraint adaptation mechanisms to ensure that validated constraints are up-to-date. This paper presents an approach based on constraint templates that tackles this evolution scenario by automatically updating constraints. We developed the Cross-Layer Modeler XLM approach which relies on incremental consistency-checking. As a case study, we performed evolutions of the UML-metamodel and 21 design models. Our approach is sound and the empirical evaluation shows that it is near instant and scales with increasing model sizes.

Co-evolution of metamodels and models through consistent change propagation

The concept of consistent change propagation (CCP) is introduced.CCP is applied to the issue of co-evolving metamodels and models.Evaluation on UML metamodel and UML models shows scalability and efficiency. In model-driven engineering (MDE), metamodels and domain-specific languages are key artifacts as they are used to define syntax and static semantics of domain models. However, metamodels are evolving over time, requiring existing domain models to be co-evolved. Though approaches have been proposed for performing such co-evolution automatically, those approaches typically support only specific metamodel changes. In this paper, we present a vision of co-evolution between metamodels and models through consistent change propagation. The approach addresses co-evolution issues without being limited to specific metamodels or evolution scenarios. It relies on incremental management of metamodel-based constraints that are used to detect co-evolution failures (i.e., inconsistencies between metamodel and model). After failure detection, the approach automatically generates suggestions for correction (i.e., repairs for inconsistencies). A case study with the UML metamodel and 23 UML models shows that the approach is technically feasible and also scalable.

Constraint-Driven modeling through transformation

In model-driven software engineering, model transformations play a key role since they are used to automatically generate and update models from existing information. However, defining concrete transformation rules is a complex task because the designer has to cope with incompleteness, ambiguity, bidirectionality, and rule dependencies. In this paper, we propose a vision of Constraint-driven Modeling in which transformation is used to automate the generation of model constraints instead of generating entire models. Three illustrative scenarios show how this approach addresses common transformation issues and how designers can benefit from using model constraints and guidance. We developed a proof-of-concept implementation that covers an important part of this vision and thus demonstrates its feasibility. The implementation also suggests that a constraint-driven transformation is efficient and scales even with increasing numbers of involved models.

Automatically generating and adapting model constraints to support co-evolution of design models

Design models must abide by constraints that can come from diverse sources, like their metamodels, requirements, or the problem domain. Software modelers expect these constraints to be enforced on their models and receive instant error feedback if they fail. This works well when constraints are stable. However, constraints may evolve much like their models do. This evolution demands efficient constraint adaptation mechanisms to ensure that models are always validated against the correct constraints. In this paper, we present an idea based on constraint templates that tackles this evolution scenario by automatically generating and updating constraints.

Constraint-driven modeling through transformation

In model-driven software engineering, model transformation plays a key role for automatically generating and updating models. Transformation rules define how source model elements are to be transformed into target model elements. However, defining transformation rules is a complex task, especially in situations where semantic differences or incompleteness allow for alternative interpretations or where models change continuously before and after transformation. This paper proposes constraint-driven modeling where transformation is used to generate constraints on the target model rather than the target model itself. We evaluated the approach on three case studies that address the above difficulties and other common transformation issues. We also developed a proof-of-concept implementation that demonstrates its feasibility. The implementation suggests that constraint-driven transformation is an efficient and scalable alternative and/or complement to traditional transformation.

DesignSpace: an infrastructure for multi-user/multi-tool engineering

The engineering and maintenance of large (software) systems is an inherently collaborative process that involves diverse engineering teams, heterogeneous development artifacts, and different engineering tools. While teams have to collaborate continuously and their artifacts are often related, the tools they use are nearly always independent, single-user applications. These tools range from programming to modeling tools and cover a wide range of engineering disciplines. However, relations among the artifacts across these tools often remain undocumented and are handled in an adhoc manner. Keeping these artifacts in sync continues to be a key engineering challenge. In this paper, we present our vision of the DesignSpace, a novel engineering infrastructure for integrating diverse development artifacts and their relations. The DesignSpace supports distributed collaboration, a wide range of tools and development, maintenance, and evolution services including incremental consistency checking and transformation.

Fine-tuning model transformation: change propagation in context of consistency, completeness, and human guidance

An important role of model transformation is in exchanging modeling information among diverse modeling languages. However, while a model is typically constrained by other models, additional information is often necessary to transform said models entirely. This dilemma poses unique challenges for the model transformation community. To counter this problem we require a smart transformation assistant. Such an assistant should be able to combine information from diverse models, react incrementally to enable transformation as information becomes available, and accept human guidance - from direct queries to understanding the designer(s) intentions. Such an assistant should embrace variability to explicitly express and constrain uncertainties during transformation - for example, by transforming alternatives (if no unique transformation result is computable) and constraining these alternatives during subsequent modeling. We would want this smart assistant to optimize how it seeks guidance, perhaps by asking the most beneficial questions first while avoiding asking questions at inappropriate times. Finally, we would want to ensure that such an assistant produces correct transformation results despite the presence of inconsistencies. Inconsistencies are often tolerated yet we have to understand that their presence may inadvertently trigger erroneous transformations, thus requiring backtracking and/or sandboxing of transformation results. This paper explores these and other issues concerning model transformation and sketches challenges and opportunities.

Towards Model-and-Code Consistency Checking

In model-driven engineering, design models allow for efficient designing without considering implementation details. Still, it is crucial that design models and source code are in sync. Unfortunately, both artifacts do evolve frequently and concurrently which causes them to drift apart over time. Even though technologies such as model-to-code transformations are commonly employed to keep design models and source code synchronized, those technologies typically still require unguided, manual adaptations. Hence, they do not effectively prevent inconsistencies from being introduced. In this paper, we outline a novel approach for checking consistency between design models and source code. Our approach aims at detecting inconsistencies instantly and informing developers about a projects consistency status live during development.

Introducing Traceability and Consistency Checking for Change Impact Analysis across Engineering Tools in an Automation Solution Company: An Experience Report

In todays engineering projects, companies continuously have to adapt their systems to changing customer or market requirements. This requires a flexible, iterative development process in which different parts of the system under construction are built and updated concurrently. However, concurrent engineering is quite problematic in domains where different engineering domains and different engineering tools come together. In this paper, we discuss experiences with Van Hoecke Automation, a leading company in the areas of production automation and product processing, in maintaining the consistency between electrical models and the corresponding software controller when both are subject to continuous change. The paper discusses how we let engineers describe the relationships between electrical model and software code in form of links and consistency rules, and how through continuous consistency checking our approach then notified those engineers of the erroneous impact of changes in either electrical model or code.