Towards hybrid model persistence

Change-based persistence has the potential to support faster and more accurate model comparison, merging, as well as a range of analytics activities. However, reconstructing the state of a model by replaying its editing history every time the model needs to be queried or modified can get increasingly expensive as the model grows in size. In this work, we integrate change-based and state-based persistence mechanisms in a hybrid model persistence approach that delivers the best of both worlds. In this paper, we present the design of our hybrid model persistence approach and report on its impact on time and memory footprint for model loading, saving, and storage space usage

A Survey on Self-healing Software System

With the increasing complexity of software systems, it becomes very difficult to install, configure, adjust, and maintain them. As systems become more interconnected and diverse, system architects are less able to predict and design the interaction between components, deferring the handling of these issues to runtime. One of the important problems that occur during execution is system failures, which increase the need for self-healing systems. The main purpose of self-healing is to have an automatic system that can heal itself without human intervention. This system has predefined actions and procedures that are suitable for recovering the system from different failure modes. In this study, different self-healing methods are categorized and a summary of them is presented

Consistency-by-Construction Techniques for Software Models and Model Transformations

A model is consistent with given specifications (specs) if and only if all the specifications are held on the model, i.e., all the specs are true (correct) for the model. Constructing consistent models (e.g., programs or artifacts) is vital during software development, especially in Model-Driven Engineering (MDE), where models are employed throughout the life cycle of software development phases (analysis, design, implementation, and testing). Models are usually written using domain-specific modeling languages (DSMLs) and specified to describe a domain problem or a system from different perspectives and at several levels of abstraction. If a model conforms to the definition of its DSML (denoted usually by a meta-model and integrity constraints), the model is consistent. Model transformations are an essential technology for manipulating models, including, e.g., refactoring and code generation in a (semi)automated way. They are often supposed to have a well-defined behavior in the sense that their resulting models are consistent with regard to a set of constraints. Inconsistent models may affect their applicability and thus the automation becomes untrustworthy and error-prone. The consistency of the models and model transformation results contribute to the quality of the overall modeled system. Although MDE has significantly progressed and become an accepted best practice in many application domains such as automotive and aerospace, there are still several significant challenges that have to be tackled to realize the MDE vision in the industry. Challenges such as handling and resolving inconsistent models (e.g., incomplete models), enabling and enforcing model consistency/correctness during the construction, fostering the trust in and use of model transformations (e.g., by ensuring the resulting models are consistent), developing efficient (automated, standardized and reliable) domain-specific modeling tools, and dealing with large models are continually making the need for more research evident. In this thesis, we contribute four automated interactive techniques for ensuring the consistency of models and model transformation results during the construction process. The first two contributions construct consistent models of a given DSML in an automated and interactive way. The construction can start at a seed model being potentially inconsistent. Since enhancing a set of transformations to satisfy a set of constraints is a tedious and error-prone task and requires high skills related to the theoretical foundation, we present the other contributions. They ensure model consistency by enhancing the behavior of model transformations through automatically constructing application conditions. The resulting application conditions control the applicability of the transformations to respect a set of constraints. Moreover, we provide several optimizing strategies. Specifically, we present the following: First, we present a model repair technique for repairing models in an automated and interactive way. Our approach guides the modeler to repair the whole model by resolving all the cardinalities violations and thereby yields a desired, consistent model. Second, we introduce a model generation technique to efficiently generate large, consistent, and diverse models. Both techniques are DSML-agnostic, i.e., they can deal with any meta-models. We present meta-techniques to instantiate both approaches to a given DSML; namely, we develop meta-tools to generate the corresponding DSML tools (model repair and generation) for a given meta-model automatically. We present the soundness of our techniques and evaluate and discuss their features such as scalability. Third, we develop a tool based on a correct-by-construction technique for translating OCL constraints into semantically equivalent graph constraints and integrating them as guaranteeing application conditions into a transformation rule in a fully automated way. A constraint-guaranteeing application condition ensures that a rule applies successfully to a model if and only if the resulting model after the rule application satisfies the constraint. Fourth, we propose an optimizing-by-construction technique for application conditions for transformation rules that need to be constraint-preserving. A constraint-preserving application condition ensures that a rule applies successfully to a consistent model (w.r.t. the constraint) if and only if the resulting model after the rule application still satisfies the constraint. We show the soundness of our techniques, develop them as ready-to-use tools, evaluate the efficiency (complexity and performance) of both works, and assess the overall approach in general as well. All our four techniques are compliant with the Eclipse Modeling Framework (EMF), which is the realization of the OMG standard specification in practice. Thus, the interoperability and the interchangeability of the techniques are ensured. Our techniques not only improve the quality of the modeled system but also increase software productivity by providing meta-tools for generating the DSML tool supports and automating the tasks

Tool-Support of Socio-Technical Coordination in the Context of Heterogeneous Modeling: A Research Statement and Associated Roadmap

International audienceThe growing complexity of everyday life systems (and devices) over the last decades has forced the industry to use and investigate different development techniques to manage the many different aspects of the systems. In this context, the use of model-driven engineering (MDE) has emerged and is now common practice for many engineering disciplines. However, this comes with important challenges. As a set of main challenges relates to the fact that different modeling techniques, languages, and tools are required to deal with the different system aspects, and that support is required to ensure consistency and coherence between the different models. This paper identifies a number of challenges and paints a roadmap on how tooling can support a multi-model integrated way of working

A deep recurrent Q network towards self-adapting distributed microservice architecture

One desired aspect of microservice architecture is the ability to self-adapt its own architecture and behavior in response to changes in the operational environment. To achieve the desired high levels of self-adaptability, this research implements distributed microservice architecture model running a swarm cluster, as informed by the Monitor, Analyze, Plan, and Execute over a shared Knowledge (MAPE-K) model. The proposed architecture employs multiadaptation agents supported by a centralized controller, which can observe the environment and execute a suitable adaptation action. The adaptation planning is managed by a deep recurrent Q-learning network (DRQN). It is argued that such integration between DRQN and Markov decision process (MDP) agents in a MAPE-K model offers distributed microservice architecture with self-adaptability and high levels of availability and scalability. Integrating DRQN into the adaptation process improves the effectiveness of the adaptation and reduces any adaptation risks, including resource overprovisioning and thrashing. The performance of DRQN is evaluated against deep Q-learning and policy gradient algorithms, including (1) a deep Q-learning network (DQN), (2) a dueling DQN (DDQN), (3) a policy gradient neural network, and (4) deep deterministic policy gradient. The DRQN implementation in this paper manages to outperform the aforementioned algorithms in terms of total reward, less adaptation time, lower error rates, plus faster convergence and training time. We strongly believe that DRQN is more suitable for driving the adaptation in distributed services-oriented architecture and offers better performance than other dynamic decision-making algorithms

The COVID-19 Pandemic and the Future of Working Spaces

This edited volume presents a compendium of emerging and innovative studies on the proliferation of new working spaces (NeWSps), both formal and informal (such as coworking spaces, maker spaces, fab labs, public libraries, and cofee shops), and their role during and following the COVID-19 pandemic in urban and regional development and planning. This book presents an original, interdisciplinary approach to NeWSps through three features: (i) situating the debate in the context of the COVID-19 pandemic, which has transformed NeWSp business models and the everyday work life of their owners and users; (ii) repositioning and rethinking the debate on NeWSps in the context of socioeconomics and planning and comparing conditions between before and during the COVID-19 pandemic; and (iii) providing new directions for urban and regional development and resilience to the COVID-19 pandemic, considering new ways of working and living. The 17 chapters are co-authored by both leading international scholars who have studied the proliferation of NeWSps in the last decade and young, talented researchers, resulting in a total of 55 co-authors from diferent disciplines (48 of whom are currently involved in the COST Action CA18214 ‘The Geography of New Working Spaces and Impact on the Periphery’ 2019–2023: www.newworking- spaces.eu). Selected comparative studies among several European countries (Western and Eastern Europe) and from the US and Lebanon are presented. The book contributes to the understanding of multi-disciplinary theoretical and practical implications of NeWSps for our society, economy, and urban/regional planning in conditions following the COVID-19 pandemic

The COVID-19 Pandemic and the Future of Working Spaces

This edited volume presents a compendium of emerging and innovative studies on the proliferation of new working spaces (NeWSps), both formal and informal (such as coworking spaces, maker spaces, fab labs, public libraries, and coffee shops), and their role during and following the COVID-19 pandemic in urban and regional development and planning. This book presents an original, interdisciplinary approach to NeWSps through three features: (i) situating the debate in the context of the COVID-19 pandemic, which has transformed NeWSp business models and the everyday work life of their owners and users; (ii) repositioning and rethinking the debate on NeWSps in the context of socioeconomics and planning and comparing conditions between before and during the COVID-19 pandemic; and (iii) providing new directions for urban and regional development and resilience to the COVID-19 pandemic, considering new ways of working and living. The 17 chapters are co-authored by both leading international scholars who have studied the proliferation of NeWSps in the last decade and young, talented researchers, resulting in a total of 55 co-authors from different disciplines (48 of whom are currently involved in the COST Action CA18214 ‘The Geography of New Working Spaces and Impact on the Periphery’ 2019–2023: www.new-working-spaces.eu). Selected comparative studies among several European countries (Western and Eastern Europe) and from the US and Lebanon are presented. The book contributes to the understanding of multi-disciplinary theoretical and practical implications of NeWSps for our society, economy, and urban/regional planning in conditions following the COVID-19 pandemic

Towards efficient comparison of change-based models

Comparison of large models can be time-consuming since every element has to be visited, matched, and compared with its respective element in other models. This can result in bottlenecks in collaborative modelling environments, where identifying differences between two versions of a model is desirable. Reducing the comparison process to only the elements that have been modified since a previous known state (e.g., previous version) could significantly reduce the time required for large model comparison. This paper presents how change-based persistence can be used to localise the comparison of models so that only elements affected by recent changes are compared and to substantially reduce comparison and differencing time (up to 90% in some experiments) compared to state-based model comparison