Semantic model-driven architecting of service-based software systems

Model-driven development is a software development framework that emphasises model-based abstraction and automated code generation. Service-based software architectures benefit in particular from semantic, ontology-based modelling. We present ontology-based transformation and reasoning techniques for layered semantic service architecture modelling. Integrated ontological layers support abstract domain modelling, architectural design, and interoperability aspects. Ontologies are beneficial due to their potential to formally define models, to allow reasoning about semantic models, and to automate transformations at all layers. Ontologies are suitable in particular for the Web Services platform due to their ubiquity within the Semantic Web and their application to support semantic Web services.     

Industrial Design and Development Software System Architecture Based on Model-Based Systems Engineering and Cloud Computing

Oriented to the new generation of cyber-physical manufacturing, this paper introduces two breakthroughs of industrial design and development software system: model-based systems engineering (MBSE) and cloud computing. Based on the classic V model for product development, an MBSE double-Vs model is refined which takes both model life-cycle and product life-cycle into consideration for design process re-engineering. Then cloud computing and its re-structuring on traditional industrial software system are discussed. Shift from classic single approach, this research proposes an industrial design and development software system architecture based on model-based systems engineering (MBSE) and cloud computing. The new architecture is shown in a stereoscopic way which amply utilizes the respective advantages of MBSE and cloud computing. At the end of the paper, as an example of actual cases, a flight management system (FMS) design and development platform is detailed to verify the effectiveness of this new architecture. The implement of new system architecture creates an efficient collaborative mechanism for FMS developing.     

一种软件演化过程模型的代数语义

随着大量的软件演化过程模型被软件演化过程元模型建模产生,如何验证过程模型的正确性,是摆在人们面前的一个重要任务.针对软件演化过程元模型,引入进程代数ACP(algebra of communicating processes)对其扩展,提出软件演化过程元模型代数,使用进程项指定软件演化过程模型的代数语义,在进程代数的统一框架下,基于等式推理验证软件演化过程模型的行为,使行为验证方式从模型推导变为代数推导这种方法充分结合了Petri网和ACP的长处,可以有效地支持软件演化过程的形式验证.     

CORAMOD: a checklist-oriented model-based requirements analysis approach

Requirement development activities such as requirements analysis and modelling are well defined in software engineering. A model-based requirement development may result in significant improvements in engineering design. In current product development activities in this domain, not all requirements are consciously identified and modelled. This paper presents the checklist-oriented requirements analysis modelling (CORAMOD) approach. CORAMOD is a methodology for the use of model-based systems engineering for requirements analysis of complex products utilizing checklists, the simplest kind of rational design method. The model-based focuses the requirements analysis process on requirement modelling, whereas the checklist encourages a conscious and systematic approach to identify requirements. We illustrate the utility of CORAMOD artefacts by a comprehensive case study example and modelling with system modelling language (SysML). We suggest that visual accessibility of the SysML views facilitates the full participation of all stakeholders and enables the necessary dialogue and negotiation. The approach promotes tracing derived requirements to the customer need statement and enhances validation by model execution and simulation.     

Production modelling for holistic production control

Holistic production control is a concept that introduces production optimisation by employing model-based, closed-loop control of the principal production Performance Indicators (pPIs). The concept relies on the development of a simple black-box model that describes the relation between the main pPIs and the most influential input (manipulative) variables. In this article the modelling aspects of the holistic production control implementation are presented. The main steps of the production modelling procedure are described, such as data preprocessing, the definition of pPIs, the selection of input variables and the derivation of black-box models. Particular emphasis is given to a modelling approach based on neural networks and a corresponding modelling assistant tool, which has been developed to support the modelling procedure. The approach is illustrated on the Tennessee Eastman benchmark process, where neural network models for three main production performance indicators, i.e., costs, quality and production rate, are derived.     

From monolithic to component-based performance evaluation of software architectures

Model-based performance evaluation methods for software architectures can help architects to assess design alternatives and save costs for late life-cycle performance fixes. A recent trend is component-based performance modelling, which aims at creating reusable performance models; a number of such methods have been proposed during the last decade. Their accuracy and the needed effort for modelling are heavily influenced by human factors, which are so far hardly understood empirically. Do component-based methods allow to make performance predictions with a comparable accuracy while saving effort in a reuse scenario? We examined three monolithic methods (SPE, umlPSI, Capacity Planning (CP)) and one component-based performance evaluation method (PCM) with regard to their accuracy and effort from the viewpoint of method users. We conducted a series of three experiments (with different levels of control) involving 47 computer science students. In the first experiment, we compared the applicability of the monolithic methods in order to choose one of them for comparison. In the second experiment, we compared the accuracy and effort of this monolithic and the component-based method for the model creation case. In the third, we studied the effort reduction from reusing component-based models. Data were collected based on the resulting artefacts, questionnaires and screen recording. They were analysed using hypothesis testing, linear models, and analysis of variance. For the monolithic methods, we found that using SPE and CP resulted in accurate predictions, while umlPSI produced over-estimates. Comparing the component-based method PCM with SPE, we found that creating reusable models using PCM takes more (but not drastically more) time than using SPE and that participants can create accurate models with both techniques. Finally, we found that reusing PCM models can save time, because effort to reuse can be explained by a model that is independent of the inner complexity of a component. The tasks performed in our experiments reflect only a subset of the actual activities when applying model-based performance evaluation methods in a software development process. Our results indicate that sufficient prediction accuracy can be achieved with both monolithic and component-based methods, and that the higher effort for component-based performance modelling will indeed pay off when the component models incorporate and hide a sufficient amount of complexity.     

TracED: A tool for capturing and tracing engineering design processes

The design of products or production processes in many engineering disciplines such as chemical, or software engineering, involves many creative and sometimes unstructured activities, with an opportunistic control flow. During these design processes, several models are generated, which have different levels of abstraction of the object being designed. Given the difficulties in dealing with this complexity using an improvised way, there is an urgent need for tools that support the capture and tracing of this process. In this proposal, TracED, a computational environment to support the capture and tracing of engineering design process is presented. It allows defining a particular engineering design domain and supporting the capture of how products under development are transformed along an engineering design process. Particularly, in this work, we consider software architectures design domain. As in any complex process, the support of computational tools is required for enabling its capture.     

Transition to a legacy- and reuse-based software life cycle

Augmenting domain engineering with legacy software reengineering can help an organization move into the software reuse mode. We present a generalized model of the software life-cycle that explicitly recognizes the critical contribution of legacy software to the attainment of software production from reusable software components. The model defines information products of the software life-cycle, denoted as states, leaving the choice of processes for moving between states open to various methodologies. Then we describe alternative methodologies for transitioning from current practices to the new software life-cycle. This includes evaluation criteria for enabling an informed decision concerning the selection of a transition methodology. Finally, the key to making the alternative transition methodologies feasible is the supporting automated environments. Our experience at integrating automated environments helps us to illustrate the transitions to, and operation within, the proposed software life-cycle     

基于模型的自适应方法综述

自适应为管理现代软件系统的复杂性提供了有效的解决方案,被设计为自适应系统的软件能够持续的演化以应对环境中的不确定性.在现有的研究工作中,基于模型的自适应方法是一类广泛使用的方法,它将模型驱动工程技术的应用从设计时扩展到运行时以支持自适应能力的实现.通过利用软件模型对运行时丰富和不确定的信息进行管理,这类方法避免了将自适应逻辑与程序语言交织带来的复杂性,从而简化了自适应系统的开发.本文对近些年来国内外学者在该研究领域取得的成果进行了系统总结.首先,给出了六个研究问题,包括相关工作常用的需求模型、结构模型、行为模型、环境模型、模型与模型或模型与系统间的同步方式、自适应规划算法等;接着,依次总结了相关工作在这六个研究问题上的已有研究成果;最后,对未来研究可能面临的挑战进行了展望.     

Managing Problems for Global Software Production – Experience and Lessons

With the increase in size and complexity of current software projects, many large companies have established global software production lines over the world to develop and deliver software products with collaborative software development processes involving multiple teams located at different sites. Supporting global software production needs an effective software-engineering environment to meet the special requirements of the collaborative software development process, diverse management methods and engineering practice. WWW technology provides powerful means to set up an enterprise-oriented software engineering environment for global software production due to its advantages in networking, global access, internationalization, and communication. Although there are many articles addressing the methods and experience in building web-based applications systems and tools, very few papers discuss the real-world problems and solutions in the development and deployment of web-based software tools to support a collaborative software development process for global software production. This paper discusses the real world issues, and reports our experience and lessons in building and deploying a web-based problem information management system (PIMS) to support global software development processes at Fujitsu. It focuses on the real issues and needs of current collaborative development process involving multiple teams, and highlights the benefits and impact of the PIMS on global software production. Moreover, it discusses our technical solutions and trade-offs in the development of PIMS, and shares our experience and lessons. Furthermore, it introduces a new data-centered conceptual process model to support diverse collaborative processes for project and problem management in global software production. Finally, the paper shares our key successes and weaknesses, and reports our experience and lessons in the deployment of the system.     

Integrating business and software development models

Developing the right products the right way is challenging, not least in software product development. It requires sound business decisions as well as good engineering practices. Various software development life-cycle models can be mapped to business decision models by mapping business decision gates and major development milestones. Two well-known life-cycle models have been mapped to the ABB Gate Model, a business decision model for product development projects that was developed to ensure that released products could be successfully deployed into the marketplace.     

Feature Nets: behavioural modelling of software product lines

Software product lines (SPLs) are diverse systems that are developed using a dual engineering process: (a) family engineering defines the commonality and variability among all members of the SPL, and (b) application engineering derives specific products based on the common foundation combined with a variable selection of features. The number of derivable products in an SPL can thus be exponential in the number of features. This inherent complexity poses two main challenges when it comes to modelling: firstly, the formalism used for modelling SPLs needs to be modular and scalable. Secondly, it should ensure that all products behave correctly by providing the ability to analyse and verify complex models efficiently. In this paper, we propose to integrate an established modelling formalism (Petri nets) with the domain of software product line engineering. To this end, we extend Petri nets to Feature Nets. While Petri nets provide a framework for formally modelling and verifying single software systems, Feature Nets offer the same sort of benefits for software product lines. We show how SPLs can be modelled in an incremental, modular fashion using Feature Nets, provide a Feature Nets variant that supports modelling dynamic SPLs, and propose an analysis method for SPL modelled as Feature Nets. By facilitating the construction of a single model that includes the various behaviours exhibited by the products in an SPL, we make a significant step towards efficient and practical quality assurance methods for software product lines.     

Process algebras for systems diagnosis

In this paper we propose a new characterization of model-based diagnosis based on process algebras, a framework which is widely used in several areas of computer science. We show that process algebras provide a powerful modelling language which allows us to capture, in an uniform way, different types of models of physical systems, including models of time-varying and dynamic behavior. Then we provide a characterization of diagnosis which is equivalent to the “classical” abductive one. This suggests new interesting opportunities for research on relations between model-based reasoning and process algebras.     

ProOpter: An advanced platform for production analysis and optimization

This paper presents the prototype of an advanced platform for production analysis and optimization, referred to as ProOpter. The platform was developed to support the recently derived concept of holistic production control (HPC), which relies on model-based control. The prototype is comprised of a set of off-line and on-line modules. The off-line modules support the definition of key performance indicators (KPIs), the selection of the most influential input (manipulative) variables, and the identification of a simple production model from historical data. The on-line modules enable KPI prediction and suggest actions to the production manager, employing model-based production control and/or optimization techniques. In this way, a new decision-support reasoning based on historical production data can be introduced. ProOpter has a modular design and can be used as an add-on to existing production IT systems since it relies on established industrial communication standards. The use of the platform is validated on the well-known Tennessee Eastman benchmark simulation process and on two industrial case studies.     

Complexity metrics for DMN decision models

Complexity impairs the maintainability and understandability of conceptual models. Complexity metrics have been used in software engineering and business process management (BPM) to capture the degree of complexity of conceptual models. The recent introduction of the Decision Model and Notation (DMN) standard provides opportunities to shift towards the Separation of Concerns paradigm when it comes to modelling processes and decisions. However, unlike for processes, no studies exist that address the representational complexity of DMN decision models. In this paper, we provide an initial set of complexity metrics for DMN models. We gather insights from the process modelling and software engineering fields to propose complexity metrics for DMN decision models. Additionally, we provide an empirical complexity assessment of DMN decision models. For the decision requirements level of the DMN standard 19 metrics were proposed, while 7 metrics were put forward for the decision logic level. For decision requirements, the model size-based metrics, the Durfee Square Metric (DSM) and the Perfect Square Metric (PSM) prove to be the most suitable. For the decision logic level of DMN the Hit Policy Usage (HPU) and the Total Number of Input Variables (TNIV) were evaluated as suitable for measuring DMN decision table complexity.     

Requirements Engineering-Based Conceptual Modelling

The software production process involves a set of phases where a clear relationship and smooth transitions between them should be introduced. In this paper, a requirements engineering-based conceptual modelling approach is introduced as a way to improve the quality of the software production process. The aim of this approach is to provide a set of techniques and methods to capture software requirements and to provide a way to move from requirements to a conceptual schema in a traceable way. The approach combines a framework for requirements engineering (TRADE) and a graphical object-oriented method for conceptual modelling and code generation (OO-Method). The intended improvement of the software production process is accomplished by providing a precise methodological guidance to go from the user requirements (represented through the use of the appropriate TRADE techniques) to the conceptual schema that properly represents them (according to the conceptual constructs provided by the OO-Method). Additionally, as the OO-Method provides full model-based code generation features, this combination minimises the time dedicated to obtaining the final software product.     

Qualitative methods in empirical studies of software engineering

While empirical studies in software engineering are beginning to gain recognition in the research community, this subarea is also entering a new level of maturity by beginning to address the human aspects of software development. This added focus has added a new layer of complexity to an already challenging area of research. Along with new research questions, new research methods are needed to study nontechnical aspects of software engineering. In many other disciplines, qualitative research methods have been developed and are commonly used to handle the complexity of issues involving human behaviour. The paper presents several qualitative methods for data collection and analysis and describes them in terms of how they might be incorporated into empirical studies of software engineering, in particular how they might be combined with quantitative methods. To illustrate this use of qualitative methods, examples from real software engineering studies are used throughout     

Virtual engineering at work: the challenges for designing mechatronic products

The product race has become an innovation race, reconciling challenges of branding, performance, time to market and competitive pricing while complying with ecological, safety and legislation constraints. The answer lies in “smart” products of high complexity, relying on heterogeneous technologies and involving active components. To keep pace with this evolution and further accelerate the design cycle, the design engineering process must be rethought. The paper presents a mechatronic simulation approach to achieve this goal. The starting point is the current virtual prototyping paradigm that is widely adopted and that continues to improve in terms of model complexity, accuracy, robustness and automated optimization. Two evolutions are discussed. A first one is the extension to multi-physics simulation answering the design needs of the inherent multi-disciplinarity of “intelligent” products. Integration of thermal, hydraulic, mechanical, haptic and electrical functions requires simulation to extend beyond the traditional CAD-FEM approach, supporting the use of system, functional and perception models. The second evolution is the integration of control functions in the products. Where current industrial practice treats mechanical system design and control design as different design loops, this paper discusses their integration in a model-based design process at all design stages, turning concepts such as software-in-the-loop and hardware-in-the-loop into basic elements of an industrial design approach. These concepts are illustrated by a number of automotive design engineering cases, which demonstrate that the combined use of perception, geometric and system models allows to develop innovative solutions for the active safety, low-emission and high-comfort performance of next-generation vehicles. This process in turn poses new challenges to the design in terms of the specification and validation of such innovative products, including their failure modes and fault-tolerant behaviour. This will imply adopting a model-based system engineering approach that is currently already common practice in software engineering.