Supporting model-driven development using a process-centered software engineering environment

The adoption of Model-Driven Development (MDD) is increasing and it is widely recognized as an important approach for building software systems. In addition to traditional development process models, an MDD process requires the selection of metamodels and mapping rules for the generation of the transformation chain which produces models and application code. In this context, software process tasks should be performed in a specific sequence, with the correct input artifacts to produce the output ones. However, existing support tools and transformation engines for MDD do not have a process-centered focus that addresses different kinds of software process activities, such as application modeling and testing to guide the developers. Furthermore, they do not enable process modeling nor the (semi) automated execution of activities during process enactment. The MoDErNE (Model Driven Process-Centered Software Engineering Environment) uses process-centered software engineering environment concepts to improve MDD process specification and enactment by using a metamodeling foundation. In MoDErNE, a software process model may be enacted several times in different software projects. This paper details the MoDErNE environment, its approach and architecture and also the case studies through which the tool was evaluated.     

Video-based personalized traffic learning

We present a video-based approach to learn the specific driving characteristics of drivers in the video for advanced traffic control. Each vehicle’s specific driving characteristics are calculated with an offline learning process. Given each vehicle’s initial status and the personalized parameters as input, our approach can vividly reproduce the traffic flow in the sample video with a high accuracy. The learned characteristics can also be applied to any agent-based traffic simulation systems. We then introduce a new traffic animation method that attempts to animate each vehicle with its real driving habits and show its adaptation to the surrounding traffic situation. Our results are compared to existing traffic animation methods to demonstrate the effectiveness of our presented approach.     

Two distribution methods for multiagent traffic simulations

Modeling and simulation play an important role in transportation networks analysis. With the widespread use of personalized real-time information sources, the behavior of the simulation depends heavily on individual travelers reactions to the received information. As a consequence, it is relevant for the simulation model to be individual-centered, and multiagent simulation is the most promising paradigm in this context. However, representing the movements of realistic numbers of travelers within reasonable execution times requires significant computational resources. It also requires relevant methods, architectures and algorithms that respect the characteristics of transportation networks. In this paper, we define two multiagent simulation models representing the existing sequential multiagent traffic simulations. The first model is fundamental diagram-based model, in which travelers do not interact directly and use a fundamental diagram of traffic flow to continuously compute their speeds. The second model is car-following based, in which travelers interact with their neighbors to adapt their speeds to their surrounding environment. Then we define patterns to distribute these simulations in a high-performance environment. The first is agent-based and distributes agents equally between available computation units. The second pattern is environment-based and partitions the environment over the different units. The results show that agent-based distribution is more efficient with fundamental diagram-based model simulations while environment-based distribution is more efficient with car following-based simulations.     

A domain model of Web recommender systems based on usage mining and collaborative filtering

Considering the increasing demand of multi-agent systems, the practice of software reuse is essential to the development of such systems. Multi-agent domain engineering is a process for the construction of domain-specific agent-based reusable software artifacts, like domain models, representing the requirements of a family of multi-agent systems in a domain, and frameworks, implementing reusable agent-based design solutions to those requirements. This article describes the domain modeling tasks of the MADEM methodology and a case study on the application of GRAMO, a MADEM technique, for the construction of the domain model of ONTOWUM, specifying the common and variable requirements of a family of Web recommender systems based on usage mining and collaborative filtering.     

元建模技术研究进展

随着UML(unified modeling language)与MDA(model driven architecture)的兴起和流行,模型已经成为软件开发的核心制品,而模型重要性的提升使得建模语言以及定义建模语言的元模型逐渐成为软件开发中的一个核心要素.软件开发往往涉及多个领域,而不同的领域往往需要不同的建模语言及其建模工具.但是,手工地为不同的建模语言开发建模工具代价高昂.元建模技术是解决这个问题的方法之一,通过元建模,可以根据领域需要定制合适的元模型以定义领域建模语言,进而自动生成支持该建模语言的建模工具.大量的工程实践表明,与领域建模以及MDA相结合,元建模可以大幅度地提高软件开发效率,基于元建模的MDA比基于通用建模语言的MDA更具潜力.在最近的几年中,元建模及其相关技术发展迅猛,不但在技术上取得了长足的进步,而且在产业界也开始出现大规模的商业应用.总结了元建模的现有研究成果,分析和比较了现有元建模工具,探讨了元建模的可能发展方向.对元建模中存在的问题进行分析,并指出了可能的解决途径.     

A domain-specific language for context modeling in context-aware systems

Context-awareness refers to systems that can both sense and react based on their environment. One of the main difficulties that developers of context-aware systems must tackle is how to manage the needed context information. In this paper we present MLContext, a textual Domain-Specific Language (DSL) which is specially tailored for modeling context information. It has been implemented by applying Model-Driven Development (MDD) techniques to automatically generate software artifacts from context models. The MLContext abstract syntax has been defined as a metamodel, and model-to text transformations have been written to generate the desired software artifacts. The concrete syntax has been defined with the EMFText tool, which generates an editor and model injector.     

Agent-based simulation of electricity markets: a survey of tools

Agent-based simulation has been a popular technique in modeling and analyzing electricity markets in recent years. The main objective of this paper is to study existing agent-based simulation packages for electricity markets. We first provide an overview of electricity markets and briefly introduce the agent-based simulation technique. We then investigate several general-purpose agent-based simulation tools. Next, we review four popular agent-based simulation packages developed for electricity markets and several agent-based simulation models reported in the literature. We compare all the reviewed packages and models and identify their common features and design issues. Based on the study, we describe an agent-based simulation framework for electricity markets to facilitate the development of future models for electricity markets.     

A domain specific language for spatial simulation scenarios

This article describes DSL3S, a domain specific modelling language for Spatial Simulation in the field of Geographic Information Systems (GIS). Techniques such as cellular automata and agent-based modelling have long been used to capture and simulate the temporal dynamics of spatial information. Tools commonly employed to implement spatial simulation models include code libraries and pre-compiled models; the former require advanced programming skills while the latter impose relevant constraints on application scope. Previous attempts to produce domain specific languages in the field have invariably resulted in new textual programming languages (e.g. SELES, NetLogo, Ocelet) that are platform specific and in some cases with weak GIS support and interoperability. DSL3S synthesises relevant concepts of spatial simulation in a UML profile, that allows the design of simulation models through the arrangement of graphical elements. An implementation of this language is also presented, that relies on Model Driven Development (MDD) tools distributed with the Eclipse IDE. This includes a code generation infrastructure, that produces ready to run simulations from DSL3S models, supported by the MASON simulation tool-kit. Finally, DSL3S models for three simple and classical simulations allows to better illustrate and discuss the usage of the language.     

Agent技术在交通管理与出行服务中的应用

交通作为实时性极强的复杂离散系统,是所有交通参与者、交通设施共同作用的结果。智能体（Agent）技术作为一种基于个体的离散系统建模方法,十分适合于解决此类系统问题。在简单介绍Agent技术的基础上,综述了Agent技术在交通控制、交通仿真、出行诱导三个方面的研究应用情况,探讨了Agent技术在交通管理和出行服务方面的研究方向,并对今后的研究提出了展望。     

Data-driven agent-based modeling,with application to rooftop solar adoption

Agent-based modeling is commonly used for studying complex system properties emergent from interactions among agents. However, agent-based models are often not developed explicitly for prediction, and are generally not validated as such. We therefore present a novel data-driven agent-based modeling framework, in which individual behavior model is learned by machine learning techniques, deployed in multi-agent systems and validated using a holdout sequence of collective adoption decisions. We apply the framework to forecasting individual and aggregate residential rooftop solar adoption in San Diego county and demonstrate that the resulting agent-based model successfully forecasts solar adoption trends and provides a meaningful quantification of uncertainty about its predictions. Meanwhile, we construct a second agent-based model, with its parameters calibrated based on mean square error of its fitted aggregate adoption to the ground truth. Our result suggests that our data-driven agent-based approach based on maximum likelihood estimation substantially outperforms the calibrated agent-based model. Seeing advantage over the state-of-the-art modeling methodology, we utilize our agent-based model to aid search for potentially better incentive structures aimed at spurring more solar adoption. Although the impact of solar subsidies is rather limited in our case, our study still reveals that a simple heuristic search algorithm can lead to more effective incentive plans than the current solar subsidies in San Diego County and a previously explored structure. Finally, we examine an exclusive class of policies that gives away free systems to low-income households, which are shown significantly more efficacious than any incentive-based policies we have analyzed to date.     

Modular artifact synthesis from domain-specific models

Domain-specific modelling reduces the gap between problem domain and solution domain. It supports modelling using constructs familiar to experts of a specific domain. Domain-specific models (DSms) are (semi)automatically transformed to various lower-level artifacts, including configuration files, documentation and executable programs. Although various aspects of model-driven development have been investigated, such as model versioning, debugging and transformation, relatively not much attention has been paid to formalise how artifacts are synthesised from DSms. State-of-the-art approaches rely on ad hoc coded generators that essentially use modelling tool APIs to programmatically iterate through internal representations of DSm entities to produce target-platform artifacts. In this work, we propose a more structured approach to artifact generation, where layered model transformations are used to modularly isolate, compile and re-combine various concerns within DSms, while maintaining traceability links between corresponding constructs at different levels of abstraction. We study and demonstrate how our approach simplifies addressing non-functional requirements (e.g., timing and resource utilisation constraints) of modern embedded systems. This is achieved through the modular synthesis of performance models from DSms. We illustrate our work by means of the synthesis of fully functional Google Android applications, performance predictions, simulations and performance measurement facilities from DSms of mobile phone applications.     

Assessing a requirements evolution approach: Empirical studies in the air traffic management domain

Requirements evolution is still a challenging problem in engineering practices. In this paper, we report the results of the empirical evaluation of a novel approach for modeling and reasoning on evolving requirements. We evaluated the effectiveness of the approach in modeling requirements evolution by means of a series of empirical studies in the air traffic management (ATM) domain. As we also wanted to assess whether the knowledge of the method and/or the application domain influences the effectiveness of the approach, the studies involved researchers, master students and domain experts with different level of knowledge of the approach and of the ATM domain. The participants have applied the approach to a real evolutionary scenario which focuses on the introduction of a new queue management tool, the Arrival MANager (AMAN) and a new network for information sharing (SWIM) connecting the main ATM actors. The results from the studies show that the modeling approach is effective in capturing requirements evolution. In addition, domain knowledge and method knowledge do not have an observable effect on the effectiveness of the approach. Furthermore, the evaluation provided us useful insights on how to improve the modeling approach.     

Modeling and simulation of overtaking behavior involving environment

Overtaking is a complex driving behavior for intelligent vehicles. Current research on modeling overtaking behavior pays little attention on the effect of environment. This paper focuses on the modeling and simulation of the overtaking behavior in virtual reality traffic simulation system involving environment information, such as road geometry and wind. First, an intelligent vehicle model is proposed to better understand environment information and traffic situation. Then, overtaking behavior model is introduced in detail, the lane changing feasibility is analyzed and the fuzzy vehicle controllers considering the road and wind effect are researched. Virtual reality traffic simulation system is designed to realize the simulation of overtaking behavior, with realistic road geometry features. Finally, simulation results show the correctness and the effectiveness of our approach.     

How human-robot collaboration impacts construction productivity: An agent-based multi-fidelity modeling approach

Though construction robots have drawn attention in research and practice for decades, human-robot collaboration (HRC) remains important to conduct complex construction tasks. Considering its complexity and uniqueness, it is still unclear how HRC process will impact construction productivity, which is difficult to handle with conventional methods such as field tests, mathematical modeling and physical simulation approaches. To this end, an agent-based (AB) multi-fidelity modeling approach is introduced to simulate and evaluate how HRC influences construction productivity. A high-fidelity model is first proposed for a scenario with one robot. Then, a low-fidelity model is established to extract key parameters that capture the inner relationship among scenarios. The multi-fidelity models work together to simulate complex scenarios. Based on the simulation model, the twofold influence of HRC on productivity, namely the supplement strategy on the worker side, and the design for proactive interaction on the robot side, are fully investigated. Experimental results show that: 1) the proposed approach is feasible and flexible for simulation of complex HRC processes, and can cover multiple collaboration and interaction modes; 2) the influence of the supplement strategy is simple when there is only one robot, where lower Check Interval (CI) and higher Supplement Limit (SL) will improve productivity. But the influence becomes much more complicated when there are more robots due to the internal competition among robots for the limited time of workers; 3) HRC has a scale effect on productivity per robot, which means the productivity improves if there are more robots and workers, even if the human-robot ratio remains the same; 4) introducing proactive interaction between robots and workers could improve productivity significantly, up to 22% in our experiments, which further depends on the supplement strategy and the human-robot ratio. Overall, this research contributes an integrated approach to simulate and evaluate HRC’s impacts on productivity as well as valuable insights on how to optimize HRC for better performance and occupational health. The proposed approach is also useful for the evaluation and development of new robots.     

Language-specific model checking of UML-RT models

Model-driven development (MDD) deals with complexities of modern software development by using models. Their verification is one of the opportunities of MDD, since it can be performed in the early stages of the development. The prevailing trend in verification of MDD models has been to translate them to an input language of one of the existing tools, most notably model checkers. Such an approach has advantages; for instance, we can use tools that achieved a higher level of maturity, including SPIN, NuSMV and Java PathFinder. However, the input languages of model checkers are typically not compatible with MDD models, which can make the translations very complex and difficult to maintain. Moreover, it is more difficult to take advantage of specific features of the structure and semantics of models to, e.g., speed up analysis. In this paper, we depart from the translational trend and present more direct and dedicated approach. We use an MDD language, namely UML-RT (used in IBM Rational Software Architect RealTime Edition), and we introduce a verification method built around its main features such as hierarchical structures, action code and asynchronous communication. In our method we use a formalization tailored to UML-RT models. This enables very easy transformation of models, but also reduces the necessary translations of verification results and directly supports the most important features of UML-RT. The proposed method includes an on-the-fly model checking algorithm based on the original CTL labeling. This algorithm is further optimized to include lazy composition. In the paper, we present all necessary components of the checking algorithms. Additionally, we also show the results of experiments with our implementation using several UML-RT models and CTL formulas. The experiments provide some evidence of the viability of a language-specific analysis of MDD models and of the effectiveness of our optimizations in certain cases.     

A classification scheme for agent based approaches to dynamic optimization

Several papers in the literature employ agent-based modeling approach for providing reasonable solutions to dynamic optimization problems (DOPs). However, these studies employ a variety of agent-based modeling approaches with different strategies and features for different DOPs. On the other hand, there is an absence in the literature of a formal representation of the existing agent-based solution strategies. This paper proposes a representation scheme indicating how the solution strategies with agent-based approach can be summarized in a concise manner. We present these in a tabular form called “Agent Based Dynamic Optimization Problem Solution Strategy” (ABDOPSS). ABDOPSS distinguishes different classes of agent based algorithms (via communication type, cooperation type, dynamism domain and etc.) by specifying the fundamental ingredients of each of these approaches with respect to problem domain (problems with dynamic objective functions, constraints and etc.). This paper also analyzes 18 generic studies in the literature employing agent-based modeling based on ABDOPSS.     

Traceability-centric model-driven object-oriented engineering

ContextObject-oriented (OO) development method is a popular paradigm in developing target systems. However, the current practices of OO analysis and design (OOAD) and implementation largely rely on human developers’ experience and expertise, making it possible less efficient and more error-prone. Hence, there is room for improving the development efficiency while preserving high quality of programs.ObjectiveModel-driven development (MDD) is a promising approach to developing programs by machine-assisted model transformation, saving human efforts and reducing the possibility of introducing program faults. Hence, it is appealing to apply key disciplines of MDD in developing OO programs.MethodIn this paper, we propose a comprehensive framework for applying MDD on OO program engineering in a rigorous and formal fashion. The framework consists of: (1) a hybrid engineering model of human and machine, (2) meta-models of OOAD artifacts, (3) traceability map with trace links, and (4) transformation rules.ResultsWe identified five platform independent models and two platform specific models, and defined formal representations for them. We identified 16 traceability links and accordingly 16 transformation rules among the eight artifacts. Through the case study, we showed that our work is feasible and applicable. We assessed our work and concluded that our work is sound, complete, and extendable. Our work established the foundation toward automatic generation of OO programs based on the traceability framework.ConclusionIt is concluded that it is essential to identify the OOAD artifacts, traceability links, and transformation rules for automatic generation of OO programs. It is also important to understand the human involvement nature in MDD and to explicitly treat them in the model transformation.     

Model-driven development of mobile applications for Android and iOS supporting role-based app variability

Rapidly increasing numbers of applications and users make the development of mobile applications to one of the most promising fields in software engineering. Due to short time to market, differing platforms, and fast emerging technologies, mobile application development faces typical challenges where model-driven development (MDD) can help. We present a modeling language and an infrastructure for the MDD of native apps in Android and iOS. Our approach allows a flexible app development on different abstraction levels: compact modeling of standard app elements such as standard data management and increasingly detailed modeling of individual elements to cover, for example, specific behavior. Moreover, a kind of variability modeling is supported such that mobile apps with variants can be developed. We demonstrate our MDD approach with several apps including a conference app, a museum guide with augmented reality functionality, and a SmartPlug.     

Evaluating Dynamic Task Mapping in Network Processor Runtime Systems

Modern network processor systems require the ability to adapt their processing capabilities at runtime to changes in network traffic. Traditionally, network processor applications have been optimized for a single static workload scenario, but recently several approaches for runtime adaptation have been proposed. Comparing these approaches and developing novel runtime support algorithms is difficult due to the multicore system-on-a-chip nature of network processors. In this paper, we present a model for network processors that can aid in evaluating different runtime support systems. The model considers workload characteristics of applications and network traffic using a queuing network abstraction. The accuracy of this analytical approach to modeling runtime systems is validated through simulation. We illustrate the effectiveness of our model by comparing the performance of two existing workload adaptation algorithms.