Abstract In today's engineering projects, companies continuously have to adapt their systems to changing customers or dynamic 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 becomes quite challenging in domains where different engineering artifacts from different disciplines come into play, such as safety‐critical cyber‐physical systems, where the involved engineering artifacts are quite heterogeneous in nature. In such systems, it is of utmost importance that different artifacts remain consistent in order to guarantee a correctly functioning end product. In this article, we discuss our experiences (with a leading company working 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 changes. The article discusses how we let engineers describe the relationships between electrical models and the corresponding software controller code in the form of links and consistency rules. Additionally, we demonstrate that how our approach, through a process of continuous consistency checking, notifies engineers about the erroneous impact of their changes in various engineering artifacts.

A common task performed in model-driven software engineering is evolving models. This task is typically performed manually during the design or implementation phase of software projects and is known to cause inconsistencies. Despite extensive research on consistency checking, existing approaches either provide abstract (i.e., incomplete) repairs only, or they require manually predefined strategies on how to repair inconsistencies. In this paper, we present a novel approach that provides concrete (i.e., executable) repairs without the need of predefined repair strategies. Furthermore, our approach proposes functions which automate the generation of concrete repairs at runtime. An empirical assessment of the approach on six case studies from industry, academia and GitHub demonstrates its feasibility, and shows that the provided concrete repairs are relevant and can fix their corresponding inconsistencies automatically.

In today's 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.

Today's software projects face an environment of continuous change and evolution. In order to handle evolution in development environments (e.g., requirements, technology) effectively, over the last decades well-established development processes have been adapted significantly and new process models have been proposed. For example, there is a wide range of agile processes which are risk-driven and which not only handle but embrace frequent change. However, agile and risk-driven processes still suffer from a lack of support for automatic and generic identification of certain risks.

Software developers work concurrently on different kinds of development artifacts such as requirements, architecture, design, or source code. To keep these development artifacts consistent, developers have a wide range of consistency checking approaches available. However, most existing consistency checkers work best in context of single tools and they are not well suited when development artifacts are distributed among different tools and are being modified concurrently by many developers.

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 project's consistency status live during development.

Software Product Lines (SPLs) have gained popularity in industry as they foster the reuse of artifacts, such as code, and reduce product development effort. Although some SPLs ensure that only valid products are configurable, those products are not necessarily optimal. For instance, they may include code that is not necessary for providing the desired functionality -- often because of erroneous traceability between features and code. Such unnecessary code may be disallowed in safety critical domains, it may lead to losses in runtime performance, or it may lead to errors during later SPL evolution. In this paper, we present an approach for automatic and incremental product optimization. Our approach leverages product functionality tests to ensure that configured products do not include unnecessary artifacts -- an automatic re-optimization of products after SPL evolution is performed incrementally. The evaluation results show that such a re-optimization takes only milliseconds.

Metamodels are commonly used in Model-Driven Engineering to define available model elements and structures. However, metamodels are likely to change during development for various reasons like requirement changes or evolving domain knowledge. Updating a metamodel typically leads to non-conformance issues with existing models. Hence, evolution strategies must be developed. Additionally, the tool implementation must also be updated to support the evolved metamodel. We propose the use of metamodel-independent tools with unified modeling concepts for working with all kinds of metamodels and models. By applying the Constraint-Driven Modeling approach and generating model constraints from metamodels automatically, we solve the described issues and enable dynamic, evolving metamodels. A prototype implementation has shown the feasibility of the approach and performance tests suggest that it also scales with increasing model sizes.

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.

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.