Understanding the Successes and Challenges of Model-Driven Software Engineering - A Comprehensive Systematic Mapping

It is an undeniable fact that software plays an important role in our lives. We use the software to play our music, to check our e-mail, or even to help us drive our car. Thus, the quality of software directly influences the quality of our lives. However, the traditional Software Engineering paradigm is not able to cope with the increasing demands in quantity and quality of produced software. Thus, a new paradigm of Model Driven Software Engineering (MDSE) is quickly gaining ground. MDSE promises to solve some of the problems of traditional Software Engineering (SE) by raising the level of abstraction. Thus, MDSE proposes the use of models and model transformations, instead of textual program files used in traditional SE, as means of producing software. The models are usually graph-based, and are built by using graphical notations – i.e. the models are represented diagrammatically. The advantages of using graphical models over text files are numerous, for example it is usually easier to deduce the relations between different model elements in their diagrammatic form, thus reducing the possibility of defects during the production of the software. Furthermore, formal model transformations can be used to produce different kinds of artifacts from models in all stages of software production. For example, artifacts that can be used as input for model checkers or simulation tools can be produced. This enables the checking or simulation of software products in the early phases of development, which further reduces the probability of defects in the final software product. However, methods and techniques to support MDSE are still not mature enough. In particular methods and techniques for model configuration management (MCM) are still in development, and no generic MCM system exists. In this thesis, I describe my research which was focused on developing methods and techniques to support generic model configuration management. In particular, during my research, I focused on developing methods and techniques for supporting model evolution and model co-evolution. Described methods and techniques are generic and are suitable for a state-based approach to model configuration management. In order to support the model evolution, I developed methods for the representation, calculation, and visualization of state-based model differences. Unlike in previously published research, where these three aspects of model differences are dealt with in separation, in my research all these three aspects are integrated. Thus, the result of model differences calculation algorithm is in the format which is described by my research on model differences representation. The same representation format of model differences is used as a basis of my approach to differences visualization. It is important to notice that the developed representation format for model differences is metamodel independent, and thus is generic, i.e., it can be used to represent differences between all graph-based models. Model co-evolution is a term that describes the problem of adapting models when their metamodels evolve. My solution to this problem has three steps. In the first step a special metamodel MMfMM is introduced. Unlike in traditional approaches, where metamodels are represented as instances of a metametamodel, in my approach the metamodels are represented by models which are instances of an MMfMM. In the second step, since metamodels are represented by models, previously defined methods and techniques for model evolution are reused to represent and calculate the metamodel differences. In the final step I define an algorithm that uses the calculated metamodel differences to adapt models conforming to the evolved metamodel. In order to validate my approaches to model evolution and model co-evolution, I have developed a tool for comparing models and visualizing resulting differences, and a tool for model co-evolution. Moreover, I have developed a method to compare tools for model comparison, and using this method I have conducted a series of experiments in which I compared the tool I developed to an industrial tool called EMFCompare. The results of these experiments are also presented in the thesis. Furthermore, in order to validate my tool and approach to model co-evolution, I have also specified and conducted several experiments. The results of these experiments are also presented in the thesis.

Model Driven Software Engineering (MDSE) promotes the use of models, rather than code, as the primary development artifacts. Models tend to be more understandable than code and can represent systems are variable levels of abstraction. MDSE promises improved code quality and engineers’ productivity. Many of those benefits have been well examined and evaluated. However, the potential implications of MDSE on software security and reliability is not well understood. Model-Driven Security (MDS) is an approach that can support the process of modeling security requirements at a high level of abstraction in the early stage of software development. In this paper, we conduct a systematic study on MDS methodologies and concepts. The scope of the review is ten years from 2008 to 2018. The study reports on the frequencies of publication over this time period to identify the MDS forums based on seven classifications: online databases, year of publications, type of publication (journal or conference paper), the geographical distribution of the researchers, the main contribution of each paper, MDS approaches, and the security concepts. The majority of studies focused on extensions to existing UML languages suggesting some limitations in the current UML standard support for security. Most studies report on empirical evaluations, and UML Class Diagrams were the most extended language.

Model Driven Software Engineering is a discipline that only recently has begun to be used in mainstream practise. At the DTCA, we use this approach successfully for a major part of the Dutch Social Benefits system, a nationwide online system serving 6 million citizens. We report on our experiences with bringing forth and maintaining this system, and on our future plans for extending this approach to other key systems of our organisation. The DTCA is responsible for collecting taxes and paying social benefits. The supporting software systems typically have a lot of functionality and process huge volumes of data. Furthermore, the systems should be easy to adapt within short notice: laws do change often, and once a change to a law has been approved by the parliament, there is only little time to implement the changes. To adapt complex systems quickly, we use Functional Model Driven Development (FMDD), a variant of Model Driven Software Engineering. The FMDD approach separates functionality from other aspects of an application, hence its name. We outline the basic way of working by describing the main artefacts and roles. We detail the some of the techniques we applied in the Social Benefits System.

The pervasiveness of ubiquitously connected smart devices are the main factors in shaping the computing. With the advent of Internet of things (IoTs), massive amount of data is being generated from different sources. The centralized architecture of cloud has become inefficient for the services provision to IoT enabled applications. For better support and services, fog layer is introduced in order to manage the IoT applications demands like latency, responsiveness, deadlines, resource availability and access time etc. of the fog nodes. However, there are some issues related to resource management and fog nodes allocation to the requesting application based on user expectations in the fog layer that need to be addressed. In this paper, we have proposed a Framework, based on Model Driven Software Engineering (MDSE) that practices Machine Learning algorithms and places fog enabled IoT applications at a most suitable fog node. MDSE is meant to develop software by exploiting the problem at domain model level. It is the abstract representation of knowledge that enhances productivity by maximization of compatibility between the systems. The proposed framework is a meta-model that prioritizes the placement requests of applications based on their required expectations and calculates the abilities of the fog nodes for different application placement requests. Rules based machine learning methods are used to create rules based on user’s requirements metrics and then results are optimized to get requesting device placement in the fog layer. At the end, a case study is conducted that uses fuzzy logic for application mapping and shows how the actual application placement will be done by the framework. The proposed meta-model reduces complexity and provides flexibility to make further enhancements according to the user’s requirement to use any of the Machine Learning approaches.

Model-driven software engineering in industrial practice has been the focus of different empirical studies and experience reports. Particularly, positive effects of model-driven software engineering have been reported in the domain of embedded and safety-critical systems.We report in this paper on the experiences of the openETCS European research project whose goal was to formalize the System Requirements Specification and to develop an open source reference implementation of the European Train Control System including open source modeling tools. Furthermore, we will discuss lessons learned, e.g., about using open source modeling toolchains in safety-critical contexts and about using the SCADE Suite for the development of the safety-critical parts.

Queries are the foundations of data intensive applications. In model-driven software engineering (MDSE), model queries are core technologies of tools and transformations. As software models are rapidly increasing in size and complexity, most MDSE tools frequently exhibit scalability issues that decrease developer productivity and increase costs. As a result, choosing the right model representation and query evaluation approach is a significant challenge for tool engineers. In the current paper, we aim to provide a benchmarking framework for the systematic investigation of query evaluation performance. More specifically, we experimentally evaluate (existing and novel) query and instance model metrics to highlight which provide sufficient performance estimates for different MDSE scenarios in various model query tools. For that purpose, we also present a comparative benchmark, which is designed to differentiate model representation and graph query evaluation approaches according to their performance when using large models and complex queries.

This proposal is about a study we recently published in the IEEE Transaction of Software Engineering journal [4]. Context: Collaborative software engineering (CoSE) deals with methods, processes and tools for enhancing collaboration, communication, and co-ordination (3C) among team members [5]. CoSE can be employed to conceive different kinds of artifacts during the development and evolution of software systems. For instance, when focusing on software design, multiple stakeholders with different expertise and responsibility collaborate on the system design. Model-Driven Software Engineering (MDSE) provides suitable techniques and tools for specifying, manipulating, and analyzing modeling artifacts including metamodels, models, and transformations [1]. Collaborative MDSE consists of methods or techniques in which multiple stakeholders manage, collaborate, and are aware of each others' work on a set of shared models. A collaborative MDSE approach is composed of three main complementary dimensions: (i) a model management infrastructure for managing the life cycle of the models, (ii) a set of collaboration means for allowing involved stakeholders to work on the modelling artifacts collaboratively, and (iii) a set of communication means for allowing involved stakeholders to exchange, share, and communicate information within the team. Collaborative MDSE is attracting several research efforts from different research areas (e.g., model-driven engineering, global software engineering, etc.), resulting in a variegated scientific body of knowledge on the topic. Objective: In this study we aim at identifying, classifying, and understanding existing collaborative MDSE approaches. More specifically, our goal is to assess (i) the key characteristics of collaborative MDSE approaches (e.g., model editing environments, model versioning mechanisms, model repositories, support for communication and decision making), (ii) their faced challenges and limitations, and (iii) the interest of researchers in collaborative MDSE approaches over time and their focus on the three dimensions of collaborative MDSE. Method: In order to achieve this, we designed and conducted a systematic mapping study [6] on collaborative MDSE. Starting from over 3,000 potentially relevant studies, we applied a rigorous selection procedure [3] resulting in 106 selected papers, further clustered into 48 primary studies, along a time span of nineteen years. A suitable classification framework has been empirically defined and rigorously applied for extracting key information from each selected study. We collated, summarized, and analyzed extracted data by applying scientifically sound data synthesis techniques. Results: In addition to a number of specific insights, our analysis revealed the following key findings: (i) there is a growing scientific interest on collaborative MDSE in the last years; (ii) multi-view modeling, validation support, reuse, and branching are more rarely covered with respect to other aspects about collaborative MDSE; (iii) different primary studies focus differently on individual dimensions of collaborative MDSE (i.e., model management, collaboration, and communication); (iv) most approaches are language-specific, with a prominence of UML-based approaches; (v) few approaches support the interplay between synchronous and asynchronous collaboration. Conclusion: This study gives a solid foundation for a thorough identification and comparison of existing and future approaches for collaborative MDSE [2]. Those results can be used by both researchers and practitioners for identifying existing research/technical gaps to attack, better scoping their own contributions to the field, or better understanding or refining existing ones.

The Nordic Workshop on Model Driven Software Engineering brings together industry practitioners and academic researchers to share experiences and latest research on model driven software engineering and development. The workshop is a forum to present experiences and ongoing research in software engineering and model-driven engineering in particular, but also functions as an event for establishing co-operative research projects between the participants and working on joint research papers. The workshop provides an open forum, reserving ample time for discussions. The workshop has 7 editions of past history. Prior instances were held in Reykjavik (Island), Ronneby (Sweden), Grimstad (Norway), Tampere (Finland), and Turku (Finland).

Context: Collaborative Model-Driven Software Engineering (MDSE) consists of methods and techniques where multiple stakeholders manage, collaborate, and are aware of each others' work on shared models. Objective: Collaborative MDSE is attracting research efforts from different areas, resulting in a variegated scientific body of knowledge. This study aims at identifying, classifying, and understanding existing collaborative MDSE approaches. Method: We designed and conducted a systematic mapping study. Starting from over 3,000 potentially relevant studies, we applied a rigorous selection procedure resulting in 106 selected papers, further clustered into 48 primary studies along a time span of 19 years. We rigorously defined and applied a classification framework and extracted key information from each selected study for subsequent analysis. Results: Our analysis revealed the following main fidings: (i) there is a growing scientific interest on collaborative MDSE in the last years; (ii) multi-view modeling, validation support, reuse, and branching are more rarely covered with respect to other aspects about collaborative MDSE; (iii) different primary studies focus differently on individual dimensions of collaborative MDSE (i.e., model management, collaboration, and communication); (iv) most approaches are language-specific, with a prominence of UML-based approaches; (v) few approaches support the interplay between synchronous and asynchronous collaboration. Conclusion: This study gives a solid foundation for classifying existing and future approaches for collaborative MDSE. Researchers and practitioners can use our results for identifying existing research/technical gaps to attack, better scoping their own contributions, or understanding existing ones.

Effective emergency response requires situational awareness and preplanning for various contingencies inherent to the catastrophe; may it be a natural disaster or man-made crisis situation. Model Driven Software Engineering has contributed to the domain of contingency planning and response in a befitting manner by providing generic and scalable models, simulating emergency scenarios, in order to enhance the skills of responders. However, a thorough literature review identified that a comprehensive, intelligent and repository based model for planning of various contingencies inherent to the emergency situation is a mile stone to be achieved. Accepting the challenge, Interactive Contingency environment creation and Response Planning System (CRIPS) is proposed, which is a model driven platform/framework and facilitates a crisis manager to create a virtual emergency environment with its diverse ingredients and shaping an effective response actions to cater it. A multi perspective feedback mechanism and centrally administered picture board, which are the essential components of any response planning system, have also been incorporated. In addition, intelligent rater and repository concepts are introduced to rate the planning of crisis manager and save this whole contingency environment for analysis and self-learning of model, making it distinctive to the previous researches. The outcome of this research is a comprehensive meta-model, which can be further extended for model based development of an effective contingency and emergency response planning system. The validity of proposed meta-model is demonstrated through a real world case study of terrorists attack on Army Public School (APS) Peshawar/Pakistan. The results prove that proposed model is capable of modeling simple as well as complex scenarios and allows a crisis manager to effectively model a response in order to deter the catastrophic effects.KeywordsContingencyMeta-modelModel drivenResponseFeedbackRatingRepository

Refactoring is the method to detecting and fixing bad smells in software. Refactoring techniques that have developed is a refactoring technique that is done on the source code. Along with the development of model driven software engineering (MDSE), it also developed the method of refactoring on the model. Refactoring method in the model is considered more effective and efficient because the detection and repair of bad smell is done at the design phase. The method of refactoring on the model evolves into a variety of techniques. Due to this, the systematic literature review is done to get the development of refactoring method on the developing model.

The main purpose of genericity in high lever programming language is to increase efficiency of software development and the safety and reusability of software. Genericity is the useful tool to implement generative software development and MDA. However, few modelling language has genericity mechanism and the mechanisms in typical programming language, say C++, Java, etc., is not sufficiently and is not ease to use. The situation in PAR platform is quite different. PAR platform supports Model Driven Software Engineering (MDE) and consists of algorithm modelling language Radl, abstract program modelling language Apla, a set of rules for the model transformation and a set of automatic transformation tools of algorithm model and program model. One of the distinct features of the PAR platform is the agile genericity mechanisms. In PAR not only a value, a data type and a computing-action (including operator, method, function and procedure, etc.) can be generic parameter, an ADT can be generic parameter also. We present new concepts, say type region, action region and ADT region, which can increase the safety of generic software obviously. The paper will pay special attention to describe the syntax and semantics of the ADT parameter. The case study describes the method to develop generic program with ADT as parameter.

Engineering research needs to be informed by (or based on) industrial needs to have impact, and industrial innovation depends on research to fill the gaps in knowledge and to pave the way for better tools, technologies, and services. In the past few years, in the Certus center at Simula Research Laboratory, we have been exploring ways to foster a closer collaboration between research and industry both to align our research with practical needs, and to further increase awareness about the important role that software engineering research plays as an enabler for innovation. This paper outlines our experiences with recent and successful research projects conducted in collaboration with the maritime and energy industries. We take a retrospective approach to examine the way we collaborated with our industry partners and elaborate the decisions that we believe contributed to the effectiveness of the collaborations. We report the lessons learned from our experience and illustrate these lessons using examples from several projects. The lessons focus on the applications of Model-Driven Engineering (MDE), as all the projects we draw on here were MDE projects. MDE is a fast growing discipline and is thought to be a key component of any scalable solution to long-standing software engineering problems. Our goal from structuring and sharing our experience is to contribute to a better understanding of how researchers and practitioners can collaborate more effectively and to gain more value from their collaborations.

In this paper, we explore how model-driven software engineering can be used in the development of heterogeneous multi-robot systems where we have different robots with different capabilities. Multiple robots can achieve more complex tasks that are impossible to achieve for a single robot alone. We propose a framework where simple actions are used as building blocks to define larger tasks that require multiple robots with different capabilities to achieve. We show how task distribution can be performed in such a system and how the robot operating system can be utilized. We also show how a user interface can be used to define multiple different missions for a team of heterogeneous robots without the need for regeneration of code and redeployment on each robot.

Model-driven software engineering is supported with the help of model transformations. At present, the technology for defining and executing uni-directional batch transformations seems to be well developed, while bi-directional and incremental transformations are more difficult to handle. In this paper, we present a bi-directional and incremental transformation tool for round-trip engineering between class diagrams and Java source code. Our focus lies on the model-driven construction of this tool with the help of a set of model transformation tools. Thus, our work serves as a case study of applying and composing existing model transformation tools.