We present a novel language-agnostic approach for integrating and preserving hand-written code in files generated via model-to-text transformation. Unlike existing approaches that only support modification of generated files in predefined locations (e.g., protected regions), the proposed approach allows users to make edits anywhere within generated files. Our hand-written code preservation technique has been implemented on top of an existing model-to-text transformation language (Epsilon's EGL). The approach is illustrated in a case study involving a code generator of Sirius Web editors and is contrasted with existing approaches such as protected regions and separation of generated and non-generated code using inheritance and delegation.

Systems safety is becoming increasingly challenging due to the presence of ever-more complex applications. Safety analysis is an important aspect of Safety-Critical Systems Engineering (SCSE) to discover problems in system design that can potentially lead to hazards with risks that may lead to accidents. Performing safety analysis requires significant manual effort — its automation has become the research focus in the critical system domain due to the increasing complexity of systems and the emergence of open adaptive systems. In this paper, we propose a novel methodology in which automated safety analysis drives the design of safety-critical systems. We delve into the specifics of our approach and the supporting tools. Additionally, we discuss the method to integrate our approach into the current practice of SCSE. The experimental results reveal that the proposed approach with its supporting tool promotes the efficiency of safety analysis significantly, whilst maintaining high degrees of correctness, coverage and scalability.

Abstract Multi-robot systems are increasingly deployed to provide services and accomplish missions whose complexity or cost is too high for a single robot to achieve on its own. Although multi-robot systems offer increased reliability via redundancy and enable the execution of more challenging missions, engineering these systems is very complex. This complexity affects not only the architecture modelling of the robotic team but also the modelling and analysis of the collaborative intelligence enabling the team to complete its mission. Existing approaches for the development of multi-robot applications do not provide a systematic mechanism for capturing these aspects and assessing the robustness of multi-robot systems. We address this gap by introducing ATLAS, a novel model-driven approach supporting the systematic design space exploration and robustness analysis of multi-robot systems in simulation. The ATLAS domain-specific language enables modelling the architecture of the robotic team and its mission and facilitates the specification of the team’s intelligence. We evaluate ATLAS and demonstrate its effectiveness in three simulated case studies: a healthcare Turtlebot-based mission and two unmanned underwater vehicle missions developed using the Gazebo/ROS and MOOS-IvP robotic platforms, respectively.

Safety analysis is an important aspect in Safety-Critical Systems Engineering (SCSE) to discover design problems that can potentially lead to hazards and eventually, accidents. Performing safety analysis requires significant manual effort --- its automation has become the research focus in the critical system domain due to the increasing complexity of systems and emergence of open adaptive systems. In this paper, we present a methodology, in which automated safety analysis drives the design of safety-critical systems. We discuss our approach with its tool support and evaluate its applicability. We briefly discuss how our approach fits into current practice of SCSE.

Multi-robot systems are increasingly deployed to provide services and accomplish missions whose complexity or cost is too high for a single robot to achieve on its own. Although multi-robot systems offer increased reliability via redundancy and enable the execution of more challenging missions, engineering these systems is very complex. This complexity affects not only the architecture modelling of the robotic team but also the modelling and analysis of the collaborative intelligence enabling the team to complete its mission. Existing approaches for the development of multi-robot applications do not provide a systematic mechanism for capturing these aspects and assessing the robustness of multi-robot systems. We address this gap by introducing ATLAS, a novel model-driven approach supporting the systematic robustness analysis of multi-robot systems in sim-illation. The ATLAS domain-specific language enables modelling the architecture of the robotic team and its mission, and facilitates the specification of the team's intelligence. We evaluate ATLAS and demonstrate its effectiveness on two oceanic exploration missions performed by a team of unmanned underwater vehicles developed using the MOOS-IvP robotic simulator.

Open-source model management frameworks such as OCL and ATL tend to focus on manipulating models built atop the Eclipse Modelling Framework (EMF), a de facto standard for domain specific modelling. MATLAB Simulink is a widely used proprietary modelling framework for dynamic systems that is built atop an entirely different technical stack to EMF. To leverage the facilities of open-source model management frameworks with Simulink models, these can be transformed into an EMF-compatible representation. Downsides of this approach include the synchronisation of the native Simulink model and its EMF representation as they evolve; the completeness of the EMF representation, and the transformation cost which can be crippling for large Simulink models. We propose an alternative approach to bridge Simulink models with open-source model management frameworks that uses an "on-the-fly" translation of model management constructs into MATLAB statements. Our approach does not require an EMF representation and can mitigate the cost of the upfront transformation on large models. To evaluate both approaches we measure the performance of a model validation process with Epsilon (a model management framework) on a sample of large Simulink models available on GitHub. Our previous results suggest that, with our approach, the total validation time can be reduced by up to 80%. In this paper, we expand our approach to support the management of Simulink requirements and dictionaries, and we improve the approach to perform queries on collections of model elements more efficiently. We demonstrate the use of the Simulink requirements and dictionaries with a case study and we evaluate the optimisations on collection queries with an experiment that compares the performance of a set of queries on models with different sizes. Our results suggest an improvement by up to 99% on some queries.

Abstract UML profiles offer an intuitive way for developers to build domain-specific modelling languages by reusing and extending UML concepts. Eclipse Papyrus is a powerful open-source UML modelling tool which supports UML profiling. However, with power comes complexity, implementing non-trivial UML profiles and their supporting editors in Papyrus typically requires the developers to handcraft and maintain a number of interconnected models through a loosely guided, labour-intensive and error-prone process. We demonstrate how metamodel annotations and model transformation techniques can help manage the complexity of Papyrus in the creation of UML profiles and their supporting editors. We presentJorvik, an open-source tool that implements the proposed approach. We illustrate its functionality with examples, and we evaluate our approach by comparing it against manual UML profile specification and editor implementation using a non-trivial enterprise modelling language (Archimate) as a case study. We also perform a user study in which developers are asked to produce identical editors using both Papyrus andJorvikdemonstrating the substantial productivity and maintainability benefits thatJorvikdelivers.

While the majority of research on Model-Based Software Engineering revolves around open-source modelling frameworks such as the Eclipse Modelling Framework, the use of commercial and closed-source modelling tools such as RSA, Rhapsody, MagicDraw and Enterprise Architect appears to be the norm in industry at present. This technical gap can prohibit industrial users from reaping the benefits of state-of-the-art research-based tools in their practice. In this paper, we discuss an attempt to bridge a proprietary UML modelling tool (PTC Integrity Modeller), which is used for model-based development of safety-critical systems at Rolls-Royce, with an open-source family of languages for automated model management (Epsilon). We present the architecture of our solution, the challenges we encountered in developing it, and a performance comparison against the tool’s built-in scripting interface. In addition, we use the bridge in a real-world industrial case study that involves the coordination with other bridges between proprietary tools and Epsilon.

The need for levels of availability and scalability beyond those supported by relational databases has led to the emergence of a new generation of purpose-specific databases grouped under the term NoSQL. In general, NoSQL databases are designed with horizontal scalability as a primary concern and deliver increased availability and fault tolerance at a cost of temporary inconsistency and reduced durability of data. To balance the requirements for data consistency and availability, organisations increasingly migrate towards hybrid data persistence architectures comprising both relational and NoSQL databases. The consensus is that this trend will only become stronger in the future; critical data will continue to be stored in ACID (largely relational) databases while non-critical data will be progressively migrated to high-availability NoSQL databases. Designing and deploying a hybrid data persistence architecture that involves a combination of relational and NoSQL databases is a complex, technically challenging and error-prone task. In this paper we outline a model-based methodology developed in the context of the EC-funded H2020 TYPHON project for designing, developing, querying and evolving such scalable architectures for persistence, analytics and monitoring of large volumes of hybrid (relational, graph-based, document-based, natural language, etc.) data, in a systematic and disciplined manner.

MATLAB/Simulink is a tool for dynamic system modelling widely used across industries such as aerospace and automotive. Model management languages such as OCL, ATL and the languages of the Epsilon platform enable the validation, model-to-model, model-to-text transformation of models but tend to focus on the Eclipse Modelling Framework (EMF), a de facto standard for domain specific modelling. As Simulink models are built on an entirely different technical stack, the current solution to manipulate them using such languages requires their transformation into an EMF-compatible representation. This approach is expensive as (a) the cost of the transformation can be crippling for large models, (b) it requires the synchronisation of the native Simulink model and its EMF counterpart, and (c) the EMF-representation may be an incomplete copy of the model potentially hampering model management operations. In this paper we propose an alternative approach that uses the MATLAB API to bridge Simulink models with existing model management languages that relies on the "on-the-fly" translation of model management language constructs into MATLAB/Simulink commands. Our approach not only eliminates the cost of the transformation and of the co-evolution of the EMF-compatible representation but also enables full access to all the aspects of Simulink models. We evaluate the performance of both approaches using a set of model validation constraints executed on a sample of the largest Simulink models available on GitHub. Our evaluation suggests that the on-the-fly translation approach can reduce the model validation time by up to 80%.

Flexible or bottom-up model-driven engineering (MDE) is an emerging approach to domain and systems modelling. Domain experts, who have detailed domain knowledge, typically lack the technical expertise to transfer this knowledge using traditional MDE tools. Flexible MDE approaches tackle this challenge by promoting the use of simple drawing tools to increase the involvement of domain experts in the language definition process. In such approaches, no metamodel is created upfront, but instead the process starts with the definition of example models that will be used to infer the metamodel. Pre-defined metamodels created by MDE experts may miss important concepts of the domain and thus restrict their expressiveness. However, the lack of a metamodel, that encodes the semantics of conforming models has some drawbacks, among others that of having models with elements that are unintentionally left untyped. In this paper, we propose the use of classification algorithms to help with the inference of such untyped elements. We evaluate the proposed approach in a number of random generated example models from various domains. The correct type prediction varies from 23 to 100% depending on the domain, the proportion of elements that were left untyped and the prediction algorithm used.

While the majority of research on Model-Based Software Engineering revolves around open-source modelling frameworks such as EMF, the use of commercial and closed-source modelling tools such as RSA, Rhapsody, MagicDraw and PTC Integrity Modeller appears to be the norm in industry at present. This technical gap can prohibit industrial users from reaping the benefits of state-of-the-art research-based tools in their practice. In this paper, we discuss an attempt to bridge a proprietary UML modelling tool (PTC Integrity Modeller), which is used for model-based development of safety-critical systems at Rolls-Royce, with an open-source family of languages for automated model management (Epsilon). We present the architecture of our solution, the challenges we encountered in developing it, and a performance comparison against the tool's built-in scripting interface.

Domain experts typically have detailed knowledge of the concepts that are used in their domain; however they often lack the technical skills needed to translate that knowledge into model-driven engineering (MDE) idioms and technologies. Flexible or bottom-up modelling has been introduced to assist with the involvement of domain experts by promoting the use of simple drawing tools. In traditional MDE the engineering process starts with the definition of a metamodel which is used for the instantiation of models. In bottom-up MDE example models are defined at the beginning, letting the domain experts and language engineers focus on expressing the concepts rather than spending time on technical details of the metamodelling infrastructure. The metamodel is then created manually or inferred automatically. The flexibility that bottom-up MDE offers comes with the cost of having nodes in the example models left untyped. As a result, concepts that might be important for the definition of the domain will be ignored while the example models cannot be adequately re-used in future iterations of the language definition process. In this paper, we propose a novel approach that assists in the inference of the types of untyped model elements using Constraint Programming. We evaluate the proposed approach in a number of example models to identify the performance of the prediction mechanism and the benefits it offers. The reduction in the effort needed to complete the missing types reaches up to 91.45% compared to the scenario where the language engineers had to identify and complete the types without guidance.