In Model-Driven Engineering (MDE), the Unified Modeling Language (UML) is often combined with the Object Constraint Language (OCL) to express constraints on models. While these constraints are typically used for validation, certain tasks-such as model search, completion, repair, or exploration–require the automatic generation of models that satisfy them. Due to the combinatorial nature of these tasks, even small models can lead to computationally complex problems. Constraint Programming (CP) offers a powerful paradigm for modeling and solving such combinatorial challenges, notably through the use of global constraints, which enable concise models supported by efficient algorithms. Although previous approaches have tackled model search using tools like Alloy or ATLc, they often rely on simplified subsets or avoid the use of global constraints. In this paper, we extend the ATLc approach by leveraging CP to the expressiveness and coverage of UML/OCL. We introduce a CP model for representing UML instances and evaluating queries, along with global constraint formulations for UML collection types (Sequence, Bag, Set, OrderedSet) and their corresponding OCL type casting operations (asSequence, asBag, asSet and asOrderedSet). Finally we provide an implementation allowing us to test our model for this subset of OCL and compare with related work.

While most model-transformation languages in Model-Driven Engineering are unidirectional, bidirectionality is valuable when artifacts need two-way synchronization. Although several bidirectional transformation engines have been developed, their behavior is generally considered more difficult to formulate and predict compared to the unidirectional case. In the bidirectionalization approach, users write the forward direction of their transformations in the same unidirectional language they are used to, and obtain a system that (besides performing the complete forward transformation) can automatically propagate in the backward direction the target updates. When possible, full bidirectionalization is desirable, but far from trivial.

In the Model-Driven Engineering (MDE) of complex systems, multiple models represent various systems' aspects. In practice, these models are often unconnected and specified using different modeling languages. Model view solutions can be employed to automatically combine such models. However, writing model view definitions is not trivial. When modeling languages are semantically distant and/or have a large number of concepts, it can quickly become difficult to manually identify the language elements to be selected, associated, or queried to build a model view. As a solution, this paper proposes an in-context Large Language Model (LLM)-based approach to assist engineers in writing model-view definitions. Notably, we rely on LLMs and Prompt Engineering techniques to automatically generate drafts of model-view definitions by providing as input only minimal information on the modeling languages to be combined. We implemented our approach by integrating the EMF Views solution for model views with the LangChain framework for LLM-based applications. To this end, we tailored LangChain to handle EMF metamodels. We validated our approach and implementation on a set of model views originally specified either in VPDL, the ViewPoint Definition Language of EMF Views, or as ATL model-to-model transformations. We compared these original model view definitions with the ones we automatically generated. The obtained results show the feasibility and applicability of our approach.

The Internet of Things (IoT) aims for connecting. This assumption brings about several software engineering challenges that constitute a serious obstacle to its wider adoption. The main feature of the IoT is genericity w.r.t the variability of software and hardware technologies. Model-driven engineering (MDE) is a paradigm that advocates using models to address software engineering problems. It can help to meet the genericity of the IoT from a software engineering perspective. Existing MDE approaches for the IoT focus only on modeling the internal behavior of things but lack a comprehensive approach dedicated to network modeling. In the present article, we introduce a network-oriented methodology based on MDE to unify the IoT’s heterogeneous concepts. Fundamentally, we avoid the intrinsic heterogeneity of the IoT by separating the network’s specification (i.e., the things, the communication scheme, and its constraints) from its concrete implementation (i.e., the low-level artifacts, such as source code and documentation). Technically, the methodology relies on a model-based domain-specific language (DSL) and a code generator. The former enables the modeling of the network’s specification, and the latter provides a procedure to generate the low-level artifacts from this specification. Our results show that this methodology makes iot’s software engineering more rigorous, helps prevent bugs earlier, and saves time.

Abstract To cope with the increased complexity of systems, models are used to capture what is considered the essence of a system. Such models are typically represented as a graph, which is queried to gain insight into the modelled system. Often, the results of these queries need to be adjusted according to updated requirements and are therefore a subject of maintenance activities. It is thus necessary to support writing model queries with adequate languages. However, in order to stay meaningful, the analysis results need to be refreshed as soon as the underlying models change. Therefore, a good execution speed is mandatory in order to cope with frequent model changes. In this paper, we propose a benchmark to assess model query technologies in the presence of model change sequences in the domain of social media. We present solutions to this benchmark in a variety of 11 different tools and compare them with respect to explicitness of incrementalization, asymptotic complexity and performance.

Abstract The last few years have witnessed a significant growth of so-called low-code development platforms (LCDPs) both in gaining traction on the market and attracting interest from academia. LCDPs are advertised as visual development platforms, typically running on the cloud, reducing the need for manual coding and also targeting non-professional programmers. Since LCDPs share many of the goals and features of model-driven engineering approaches, it is a common point of debate whether low-code is just a new buzzword for model-driven technologies, or whether the two terms refer to genuinely distinct approaches. To contribute to this discussion, in this expert-voice paper, we compare and contrast low-code and model-driven approaches, identifying their differences and commonalities, analysing their strong and weak points, and proposing directions for cross-pollination.

3 states that "Codebots [7] uses UML to specify domain models that are consumed to automatically generate target artefacts, including complete REST APIs, client libraries, Swagger API documentation, and a JSON Schema definition for each domain object."

Formal reasoning on model transformation languages allows users to certify model transformations against contracts. CoqTL includes a specification of a transformation engine in the Coq interactive theorem prover. An executable engine can be automatically extracted from this specification. Transformation contracts are proved by the user against the CoqTL specification and guaranteed to hold on the transformation running on the extracted implementation of CoqTL. The design of the transformation engine specification in CoqTL aims at easing the certification step, but this requirement harms the execution performance of the extracted engine.

Executable engines for relational model-transformation languages evolve continuously because of language extension, performance improvement and bug fixes. While new versions generally change the engine semantics, end-users expect to get backward-compatibility guarantees, so that existing transformations do not need to be adapted at every engine update.

Reducing the energy consumption of a complex, especially cyber-physical, system is a cross-cutting concern through the system layers, and typically requires long feedback loops between experts in several engineering disciplines. Having an immediate automatic estimation of the global system consumption at design-time would significantly accelerate this process, but cross-layer tools are missing in several domains.

In contract-based development of model transformations, continuous deductive verification may help the transformation developer in early bug detection. However, because of the execution performance of current verification systems, re-verifying from scratch after a change has been made would introduce impractical delays. We address this problem by proposing an incremental verification approach for the ATL model-transformation language. Our approach is based on decomposing each OCL contract into sub-goals, and caching the sub-goal verification results. At each change we exploit the semantics of relational model transformation to determine whether a cached verification result may be impacted. Consequently, less postconditions/sub-goals need to be re-verified. When a change forces the re-verification of a postcondition, we use the cached verification results of sub-goals to construct a simplified version of the postcondition to verify. We prove the soundness of our approach and show its effectiveness by mutation analysis. Our case study presents an approximate 50% reuse of verification results for postconditions, and 70% reuse of verification results for sub-goals. The user perceives about 56% reduction of verification time for postconditions, and 51% for sub-goals.

Some areas in computer science are characterized by a shared base structure for data artifacts (e.g., list, table, tree, graph, model), and dedicated languages for transforming this structure. We observe that in several of these languages it is possible to identify a clear correspondence between some elements in the transformation code and the output they generate. Conversely given an element in an output artifact it is often possible to immediately trace the transformation parts that are responsible for its creation. In this paper we formalize this intuitive concept by defining a property that characterizes several transformation languages in different domains. We name this property additivity: for a given fixed input, the addition or removal of program elements results in a corresponding addition or removal of parts of the output. We provide a formal definition for additivity and argue that additivity enhances modularity and incrementality of transformation engineering activities, by enumerating a set of tasks that this property enables or facilitates. Then we describe how it is instantiated in some well-known transformation languages. We expect that the development of new formal results on languages with additivity will benefit from our definitions.

Efficient processing of very large models is a key requirement for the adoption of Model-Driven Engineering (MDE) in some industrial contexts. One of the central operations in MDE is rule-based model transformation (MT). It is used to specify manipulation operations over structured data coming in the form of model graphs. However, being based on computationally expensive operations like subgraph isomorphism, MT tools are facing issues on both memory occupancy and execution time while dealing with the increasing model size and complexity. One way to overcome these issues is to exploit the wide availability of distributed clusters in the Cloud for the distributed execution of MT. In this paper, we propose an approach to automatically distribute the execution of model transformations written in a popular MT language, ATL, on top of a well-known distributed programming model, MapReduce. We show how the execution semantics of ATL can be aligned with the MapReduce computation model. We describe the extensions to the ATL transformation engine to enable distribution, and we experimentally demonstrate the scalability of this solution in a reverse-engineering scenario.

Within a given modeling platform, modeling tools interoperate efficiently. They are generally written in the same general purpose language, and use a single modeling framework (i.e., an API to access models). However, interoperability between tools from different modeling platforms is much more problematic. In this paper, we argue that fUML may be leveraged to address this issue by providing a common execution language, and by abstracting modeling frameworks into generic actions that perform elementary operations on models. Not only can user models benefit from a unified execution semantics, but modeling tools can too.

Model transformations are an integral part of several computing systems that manipulate interconnected graphs of objects called models in an input domain specified by a metamodel and a set of invariants. Test models are used to look for faults in a transformation. A test model contains a specific set of objects, their interconnections and values for their attributes. Can we automatically generate an effective set of test models using knowledge from the transformation? We present a white-box testing approach that uses static analysis to guide the automatic generation of test inputs for transformations. Our static analysis uncovers knowledge about how the input model elements are accessed by transformation operations. This information is called the input metamodel footprint due to the transformation. We transform footprint, input metamodel, its invariants, and transformation pre-conditions to a constraint satisfaction problem in Alloy. We solve the problem to generate sets of test models containing traces of the footprint. Are these test models effective? With the help of a case study transformation we evaluate the effectiveness of these test inputs. We use mutation analysis to show that the test models generated from footprints are more effective (97.62% avg. mutation score) in detecting faults than previously developed approaches based on input domain coverage criteria (89.9% avg.) and unguided generation (70.1% avg.).

This paper addresses the problem of defining a Web design process suitable for the multi-cultural audience of a Web application in the globalization era. Traditional usability guidelines implicitly depend on the background of the audience, and may overlook specific preferences dictated by the cultural context. To overcome this limit, the notion of culturability has been proposed, which extends traditional usability to take into account usage preferences stemming from multiple cultural conventions. The paper builds upon a case study (the comparison of Chinese and Western e-commerce Web sites) and proposes a working definition of culturability, based on cultural markers; moreover, an effective methodology for identifying cultural markers and turning them into design guidelines is established and validated by letting groups of users with different cultures evaluate exemplary Web applications incorporating different versions of the candidate cultural markers.