Environments to support collaborative software engineering

With increasing globalisation of software production, widespread use of software components, and the need to maintain software systems over long periods of time, there has been a recognition that better support for collaborative working is needed by software engineers. In this paper, two approaches to developing improved system support for collaborative software engineering are described: GENESIS and OPHELIA. As both projects are moving towards industrial trials and eventual publicreleases of their systems, this exercise of comparing and contrasting our approaches has provided the basis for future collaboration between our projects particularly in carrying out comparative studies of our approaches in practical use

Model Driven Engineering and Dependability Analyses: The Topcased Approach

International audienceModel Driven Engineering approaches are widely promoted to overcome difficulties to design, validate and maintain large complex systems. They present interesting dependability characteristics especially in terms of prevention of design faults and validation of design correctness. However industrial needs, practices and applicable standards impose constraints on the dependability activities to perform and justify. Therefore it is necessary to analyze how a complete dependability and safety process can be integrated with model-driven approaches within a seamless global process: which dependability activities are naturally covered or facilitated by model-driven approaches, and which additional activities are needed with which support. This paper presents the results of a study aiming at the establishment of requirements to model-driven engineering methods and tools, to support dependability analyses

Artificial Intelligence in Process Engineering

In recent years, the field of Artificial Intelligence (AI) is experiencing a boom, caused by recent breakthroughs in computing power, AI techniques, and software architectures. Among the many fields being impacted by this paradigm shift, process engineering has experienced the benefits caused by AI. However, the published methods and applications in process engineering are diverse, and there is still much unexploited potential. Herein, the goal of providing a systematic overview of the current state of AI and its applications in process engineering is discussed. Current applications are described and classified according to a broader systematic. Current techniques, types of AI as well as pre- and postprocessing will be examined similarly and assigned to the previously discussed applications. Given the importance of mechanistic models in process engineering as opposed to the pure black box nature of most of AI, reverse engineering strategies as well as hybrid modeling will be highlighted. Furthermore, a holistic strategy will be formulated for the application of the current state of AI in process engineering

Using Process Mining and Model-driven Engineering to Enhance Security of Web Information Systems

Due to the development of Smart Cities and Internet of Things, there has been an increasing interest in the use of Web information systems in different areas and domains. Besides, the number of attacks received by this kind of systems is increasing continuously. Therefore, there is a need to strengthen their protection and security. In this paper, we propose a method based on Process Mining and Model- Driven Engineering to improve the security of Web information systems. Besides, this method has been applied to the SID Digital Library case study and some preliminary results to improve the security of this system are described

Digital twin reference model development to prevent operators' risk in process plants

In the literature, many applications of Digital Twin methodologies in the manufacturing, construction and oil and gas sectors have been proposed, but there is still no reference model specifically developed for risk control and prevention. In this context, this work develops a Digital Twin reference model in order to define conceptual guidelines to support the implementation of Digital Twin for risk prediction and prevention. The reference model proposed in this paper is made up of four main layers (Process industry physical space, Communication system, Digital Twin and User space), while the implementation steps of the reference model have been divided into five phases (Development of the risk assessment plan, Development of the communication and control system, Development of Digital Twin tools, Tools integration in a Digital Twin perspective and models and Platform validation). During the design and implementation phases of a Digital Twin, different criticalities must be taken into consideration concerning the need for deterministic transactions, a large number of pervasive devices, and standardization issues. Practical implications of the proposed reference model regard the possibility to detect, identify and develop corrective actions that can affect the safety of operators, the reduction of maintenance and operating costs, and more general improvements of the company business by intervening both in strictly technological and organizational terms

Guidelines for the implementation of workforce planning (WFP) in project-driven environments

A core activity of human resource management, facing the huge challenge of matching the staffing needs in terms of right amount of skilled workers at the right moment, so as to make the whole organization able to deliver a project within a scope, on time, and budget

Screening of energy efficient technologies for industrial buildings' retrofit

This chapter discusses screening of energy efficient technologies for industrial buildings' retrofit