Model-Driven Engineering of Self-Adaptive Software with EUREMA

The integration of domain-specific concepts in a model-driven engineering (MDE) approach raises a number of interesting research questions. There are two possibilities to represent these concepts. The first one focuses on models that contain domain-specific concepts only, i.e. domain-specific modelling languages (DSML). The second one advocates the integration of domain-specific concepts in general-purpose models, using what we will refer to in this paper as domain-specific modelling annotation languages (DSMAL). In this position paper, we argue that each approach is particularly suited for specific activities and specific actors, and show how they can be developed and used together. We also highlight the challenges created by the use of two representations, such as the evaluation of models OCL constraints and the synchronisation between the two representations. As an illustration, we present rbacUML, our approach for integrating role-based access control (RBAC) concepts into an MDE approach.

Model-Driven Engineering (MDE) is changing the way we build, operate, and maintain our software-intensive systems. Several projects using MDE practices are reporting significant improvements in quality and performance but, to be able to handle these projects, software engineers need a set of technical and interpersonal skills that are currently not widely available. The MDE postgraduate diploma intends to fill this gap by offering a full-time one year formation onMDE. The course syllabus is designed to teach students how to work at a higher abstraction level by the rigorous use of (software) models as the main artifacts in all software engineering activities. Contents include the conceptual framework of MDE plus all techniques and tools (e.g. for defining modeling languages, models, and model transformations) required to successfully complete software engineering projects following a MDE approach. The organizational impact (and challenges) of adopting MDE are exemplified using real industrial experiences. This article describes the MDE Diploma and the lessons that we learned after completing its first edition.

During the last decade, Model Driven Engineering (MDE) has been proposed for supporting the development, maintenance and evolution of software systems. Model Driven Architecture (MDA), Software Factories and Eclipse Modeling Framework (EMF) are among the most representatives MDE approaches. Nowadays, it is well recognized that model transformation is at the heart of MDE approaches and, consequently represents one of the most important operations in MDE. However, despite the multitude of model transformation language proposals emerging from academic world and industry, these transformations are often manually specified; which is a tedious and error-prone task, and therefore an expensive process. Matching operation between metamodels is the keystone toward a (semi-)automatic transformation process. In this paper, the authors review metamodel matching techniques of the literature and then analyze their pros and cons in order to show how they can be useful for a semi-automatic transformation process. The result is a comparison of metamodel matching techniques, highlighting their similarities and differences in terms of information used for matching, demonstrating significant similarities between these techniques. Next, the authors compare four well-known metamodel matching techniques namely Similarity flooding, SAMT4MDE+ (extended Semi-Automatic Matching Tool for Model Driven Engineering), ModelCVS and AML (AtlanMod Matching Language) on ten couples of metamodels. For this comparison, the authors define a set of six criteria inspired from the database schema matching. One among these criteria is relevant to the quality of matching and for which we define a quality measure metrics. Furthermore, the authors develop a plug-in under Eclipse to support our comparison using ten couples of metamodels.

Hardware-in-the-loop (HIL) testing is an integral part of the development of power system automation solutions. HIL test beds facilitate system-level testing and validation of controllers against their specifications. The information required for HIL test bed configurations is typically scattered across different tools used in the development of power system automation solutions. These tools often have different formats and naming schemes. This implies that a significant effort is required for consolidating data from the tools and for creating valid test configurations from the consolidated data. This paper presents a Model-Driven Engineering (MDE) approach for creating HIL test configurations in a tool-, platform-, and protocol-agnostic manner. It shows how the MDE approach can be applied in practice to derive test configurations based on the information originating from different tools in a semi-automated fashion.

The specific requirements of developing Internet of Things (IoT) software for public transportation systems should be considered in addition to the problems always observed in IoT system development. After defining the problems encountered in the development of IoT-based public transport systems, the current model-driven engineering (MDE) approaches for IoT development are examined and their applicability for public transport systems is questioned in this paper. Several points of view are given about what new MDE approaches specific to this field should include in order to eliminate the identified deficiencies. Moreover, it is indicated that new MDE approaches should be designed including round-trip engineering to provide high mobility, use of special standards, in-vehicle communication networks and support for different system-on-chips in these public transportation systems.

The virtue of quality is not itself a subject; it depends on a subject. In the software engineering field, quality means good software products that meet customer expectations, constraints, and requirements. Despite the numerous approaches, methods, descriptive models, and tools, that have been developed, a level of consensus has been reached by software practitioners. However, in the model-driven engineering (MDE) field, which has emerged from software engineering paradigms, quality continues to be a great challenge since the subject is not fully defined. The use of models alone is not enough to manage all of the quality issues at the modeling language level. In this work, we present the current state and some relevant considerations regarding quality in MDE, by identifying current categories in quality conception and by highlighting quality issues in real applications of the model-driven initiatives. We identified 16 categories in the definition of quality in MDE. From this identification, by applying an adaptive sampling approach, we discovered the five most influential authors for the works that propose definitions of quality. These include (in order): the OMG standards (e.g., MDA, UML, MOF, OCL, SysML), the ISO standards for software quality models (e.g., 9126 and 25,000), Krogstie, Lindland, and Moody. We also discovered families of works about quality, i.e., works that belong to the same author or topic. Seventy-three works were found with evidence of the mismatch between the academic/research field of quality evaluation of modeling languages and actual MDE practice in industry. We demonstrate that this field does not currently solve quality issues reported in industrial scenarios. The evidence of the mismatch was grouped in eight categories, four for academic/research evidence and four for industrial reports. These categories were detected based on the scope proposed in each one of the academic/research works and from the questions and issues raised by real practitioners. We then proposed a scenario to illustrate quality issues in a real information system project in which multiple modeling languages were used. For the evaluation of the quality of this MDE scenario, we chose one of the most cited and influential quality frameworks; it was detected from the information obtained in the identification of the categories about quality definition for MDE. We demonstrated that the selected framework falls short in addressing the quality issues. Finally, based on the findings, we derive eight challenges for quality evaluation in MDE projects that current quality initiatives do not address sufficiently.

Despite the existence of many agent programming environments and platforms, the developers may still encounter difficulties on implementing Multi-agent Systems (MASs) due to the complexity of agent features and agent interactions inside the MAS organizations. Working in a higher abstraction layer and modeling agent components within a model-driven engineering (MDE) process before going into depths of MAS implementation may facilitate MAS development. Perhaps the most popular way of applying MDE for MAS is based on creating Domain-specific Modeling Languages (DSMLs) with including appropriate integrated development environments (IDEs) in which both modeling and code generation for system-to-be-developed can be performed properly. Although IDEs of these MAS DSMLs provide some sort of checks on modeled systems according to the related DSML’s syntax and semantics descriptions, currently they do not have a built-in support for debugging these MAS models. That deficiency causes the agent developers not to be sure on the correctness of the prepared MAS model at the design phase. To help filling this gap, we introduce a conceptual generic debugging framework supporting the design of agent components inside the modeling environments of MAS DSMLs. The debugging framework is composed of 4 different metamodels and a simulator. Use of the proposed framework starts with modeling a MAS using a design language and transforming design model instances to a run-time model. According to the framework, the run-time model is simulated on a built-in simulator for debugging. The framework also provides a control mechanism for the simulation in the form of a simulation environment model.

Nowadays, Web Applications (WAs) are complex software sys- tems, used by multiple users with different roles and often devel- oped to support and manage business processes. Due to the chang- ing nature of the supported processes, WAs need to be easily and quickly modified, to adapt and align them to the processes they support. In recent years, Model Driven Engineering (MDE) ap- proaches have been proposed and used to develop and evolveWAs. However, the definition of appropriate MDE approaches for the de- velopment of flexible process-centric WAs is still limited. In par- ticular, (flexible) workflow models have never been integrated with the models (e.g., presentation, information models) used in MDE approaches to develop this type of applications. In this paper, we present M3D (Model Driven Development with Declare), a tool for developingWAs that integrates three MDE metamodels used to represent the main components of aWA with the metamodel of De- clare, a declarative language to model business processes. The tool exploits and combines the declarative nature of Declare and the ad- vantages of MDE to get an efficient roundtrip engineering support to develop and evolve flexible process-centric WAs.

Defining low power design intent for a system-on-chip (SoC) consists in specifying its power management architecture and strategy according to specific low power techniques such as power gating and multi-voltage scaling requirements. Choosing the most-energy efficient power intent for a final system contributes widely to reduce its overall power consumption. At Transaction-Level, a rapid exploration of different power intent alternatives can be made. In this paper, we present a Model Driven Engineering (MDE) approach to automate low power design intent specifications and accelerate Low Power Design Intent Space Exploration (LPDISE) using a Transaction-Level power-aware design methodology. This MDE approach mainly relies on a high level abstraction of the Unified Power Format (UPF) standard concepts that fit a TLM approach use. Then, the MDE approach is applied to automatically generate a UPF code defining the most energy-efficient power intent and being a reference file for Register Transfer Level (RTL) design team. This task focuses on a smart deduction of adequate UPF commands from the high level abstraction semantics. The effectiveness of the proposed MDE approach is illustrated by an example.

Today's Web Applications (WAs) are complex multi-user and multi-tenant software systems, used by users with different roles and often developed to support and manage complex business processes. Due to the changing nature of such processes, WAs need to be easily and quickly modified, to be adapted and aligned to the processes they support. In recent years, Model Driven Engineering (MDE) approaches have been used to support the development and the evolution of WAs. However, the definition of appropriate MDE approaches for the development of flexible process-centric WAs is still limited. In particular, flexible workflow models have never been integrated with the models used in MDE approaches to develop this type of applications. This paper proposes an MDE approach for the development and the evolution of flexible process-centric WAs that integrates four MDE metamodels used to represent the main components of a WA with the metamodel of Declare, a declarative language to model business processes. A case study, carried out to validate the feasibility, the correctness and the effectiveness of the proposed approach is presented and discussed.

Model Driven Engineering (MDE) is a software development approach family based on the use of models in the software construction. It allows the exploitation of models to simulate, estimate, understand, communicate and produce code.Mobile agents are a very interesting technology to develop applications for mobile and distributed environments. A mobile agent is essentially a computer program that acts autonomously on behalf of a user and travels through a network of heterogeneous computers. By moving to remote computers, the mobile agent processes data there and hence saves remote communications and local computation.Many mobile-agent platforms have been developed. While some of them have been abandoned, others continue to release new versions that fix bugs detected or offer new interesting features.In this paper, we present a reflection on how one can realize the migration operation when the mobile-agent applications are built using an MDE approach.

Due to the large increase of complexity in software development in recent years, academia and organizations have a rational solution in software engineering, called Model-Driven Engineering, that seeks to support managing this complexity. Model-Driven Engineering is an approach that moves the focus of the development of software from code to models. The UML is currently the most widely used language for software modeling.

Model-driven engineering (MDE) is a software engineering discipline focusing on models as the primary artifacts in the software development process while programs are mainly generated by means of model-to-code transformations. In particular, modeling languages tailored to specific application domains promise to increase the productivity and quality of software development. Nevertheless due to e.g. evolving requirements, modeling languages and their meta-models evolve which means that existing models have to be migrated correspondingly. In our approach, such co-evolutions are specified as related graph transformations ensuring well-typed model migration results. Based on our earlier work on co-transformations, we now consider the automatic deduction of migration rule schemes from given meta-model evolution rules. Rule schemes form the basis for user customizations on a high abstraction level. A rule scheme deduction algorithm is presented and several customized migration schemes for different co-evolution examples are discussed.

Massively Parallel Multi-Processors System-on-Chip (MP2SoC) architectures have been widely deployed to run challenging high-performance computations. However, the ever greater demand for energy efficiency fosters energy budgeting in MP2SoC systems. Nowadays, having the appropriate Electronic Design Automation (EDA) tools for power estimation is mandatory. The major challenge for the design of such tools is to reach a better tradeoff between accuracy and time-to-market. This paper presents a Model Driven Engineering (MDE)-based energy-aware Design Space Exploration (DSE) approach allowing the designer to take the power consumption criterion into account early in the design flow. The originality of this approach is that it integrates the Energy-Aware Duplication (EAD) algorithm that strives to balance schedule lengths and energy savings by considering the most important sources of energy consumption in MP2SoC: the massive number of processing elements (PE) and the high-speed Network-on-Chip (NoC). To demonstrate the effectiveness of the proposed approach, we conducted experiments using the H.263 encoder application. The obtained results demonstrated that EAD can effectively save energy in MP2SoC systems. They also showed that our MDE approach is capable of accelerating the DSE process to make early energy-efficient design decisions.

DevOps is the new approach in software development that has encouraged interaction between the development and operation teams. DevOps also involves using feedback mechanisms that enhance continuous and rapid cycle feedback. However, what often occurs is that these feedback loops must be managed manually, which takes time and can be prone to mistakes. This paper explores AI and ML's ability to perform feedback loops in DevOps pipelines. In the context of the current study, we investigate the application of big data and real-time monitoring to improve code quality problem detection and prediction of performance consequences. We illustrate how feedback to developers about the necessary changes can be provided through ML models to analyze data obtained from different sources like application logs, monitoring tools, and user interactions. The paper explains the basics needed to establish feedback loops based on ML, which include data acquisition, data cleaning, model training, and online prediction. We also discuss the issues and concerns when using AI/ML in DevOps, such as the model's interpretability, the tool's integration, and change management. In this article, we explain how AI can make feedback loops smarter and provide case studies and real-life examples of how this can help improve code quality, solve problems more quickly, and enhance the relationship between development and operations. Last but not least, we discuss potential research directions for this area's further development, such as approaches to improve model interpretability, integrating collaborative learning into the DevOps process, and creating reference models for AI adoption in DevOps pipelines. In this paper, we use AI and ML to map out ways for organizations to improve their DevOps processes and ensure a feedback loop at every stage.