Book review: Basics in medical education

Software evolves continuously. As a consequence, software systems tend to become increasingly complex and, as such, more difficult to change. A software system's complexity is for a large part determined by its structure, or architecture. In this thesis we investigate how to reduce the risks and costs associated with the evolution of software architectures. Automation and abstraction are two basic software engineering techniques to deal with complexity. In this thesis we investigate the applicability of model-driven engineering, a new software development approach based on abstraction and automation, to support the evolution of software architectures. The main research question we address in this thesis is: "How can evolution of software architectures be supported?". Three subquestions related to industrial integration, software product lines, and automation further clarify the scope of our work. We first conducted a survey among several software development organisation to inventory the state-of-the-practice in software engineering technologies. Some trends we observed from this inventory include: the informal use modelling in industry, the use of product-line approaches, and the importance of the evolutionary aspect of software. Next, we investigated how to support four tasks related to software architecture evolution: evaluation, conformance checking, migration, and documentation. We aim to automate this support where possible. To this end, we employ model-driven software development technologies. For each of the software evolution tasks, we present a case study that investigates how that task can be supported. The informal use of modelling in industry calls for a normalisation step to enable the integration of evolution support in practice. Several chapters address the impact of the use of product-line approaches on the evolution support. Although the increased scope make such support more difficult to develop, the return on investment for the model-driven support is much improved. The model-driven evolution support follows a similar three-step pattern. First, a set of source models is preprocessed into a form suitable for the application of model transformations. Then, model transformations are applied that do the actual work, such as conformance checking or a migration. Finally, the resulting models are postprocessed in a resulting into a desired target form.Electrical Engineering, Mathematics and Computer Scienc

The Unfulfilled Potential of Data-Driven Decision Making in Agile Software Development

With the general trend towards data-driven decision making (DDDM), organizations are looking for ways to use DDDM to improve their decisions. However, few studies have looked into the practitioners view of DDDM, in particular for agile organizations. In this paper we investigated the experiences of using DDDM, and how data can improve decision making. An emailed questionnaire was sent out to 124 industry practitioners in agile software developing companies, of which 84 answered. The results show that few practitioners indicated a widespread use of DDDM in their current decision making practices. The practitioners were more positive to its future use for higher-level and more general decision making, fairly positive to its use for requirements elicitation and prioritization decisions, while being less positive to its future use at the team level. The practitioners do see a lot of potential for DDDM in an agile context; however, currently unfulfilled

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Automated analysis of feature models: Quo vadis?

Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of Automated Analysis of Feature Models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.Ministerio de Economía y Competitividad TIN2015-70560-RJunta de Andalucía TIC-186

Discrete event simulation and virtual reality use in industry: new opportunities and future trends

This paper reviews the area of combined discrete event simulation (DES) and virtual reality (VR) use within industry. While establishing a state of the art for progress in this area, this paper makes the case for VR DES as the vehicle of choice for complex data analysis through interactive simulation models, highlighting both its advantages and current limitations. This paper reviews active research topics such as VR and DES real-time integration, communication protocols, system design considerations, model validation, and applications of VR and DES. While summarizing future research directions for this technology combination, the case is made for smart factory adoption of VR DES as a new platform for scenario testing and decision making. It is put that in order for VR DES to fully meet the visualization requirements of both Industry 4.0 and Industrial Internet visions of digital manufacturing, further research is required in the areas of lower latency image processing, DES delivery as a service, gesture recognition for VR DES interaction, and linkage of DES to real-time data streams and Big Data sets

Special Session on Industry 4.0

No abstract available

Ready for Tomorrow: Demand-Side Emerging Skills for the 21st Century

As part of the Ready for the Job demand-side skill assessment, the Heldrich Center explored emerging work skills that will affect New Jersey's workforce in the next three to five years. The Heldrich Center identified five specific areas likely to generate new skill demands: biotechnology, security, e-learning, e-commerce, and food/agribusiness. This report explores the study's findings and offers recommendations for improving education and training in New Jersey

Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security