Cyber physical systems implementation for asset management improvement: A framework for the transition

Libro en Open AccessThe transformation of the industry due to recent technologies introduction is an evolving process whose engines are competitiveness and sustainability, understood in its broadest sense (environmental, economic and social). This process is facing, due to the current state of scientific and technological development, a new challenge yet even more important: the transition from discrete technological solutions that respond to isolated problems, to a global conception where the assets, plant, processes and engineering systems are conceived, designed and operated as an integrated complex unit. This vision is evolving besides a set of concepts that are, in some way, to guide this development: Smart Factories, Cyber-Physical Systems, Factory of the Future or Industry 4.0, are examples. The full integration of the operation and maintenance (O&M) processes in the production systems is a key topic within this new paradigm. Not only that, this evolution necessarily results in the emergence of new processes and needs of O&M, i.e. also, the O&M will undergo a profound transformation. The transition from actual isolated production assets to such Industry 4.0 with CPS is far from easy. This document presents a proposal to develop such transition adapting one iteration of the Model of Maintenance Management (MMM) integrated into ISO 55000 to the complexity of incorporating “System of Systems” CPSs maintenance. It involves several stages: identification, prioritization, risk management, planning, scheduling, execution, control, and improvement supported by system engineering techniques and agile/concurrent project managemen

Integrated data model and DSL modifications

Companies are increasingly more and more dependent on distributed web-based software systems to support their businesses. This increases the need to maintain and extend software systems with up-to-date new features. Thus, the development process to introduce new features usually needs to be swift and agile, and the supporting software evolution process needs to be safe, fast, and efficient. However, this is usually a difficult and challenging task for a developer due to the lack of support offered by programming environments, frameworks, and database management systems. Changes needed at the code level, database model, and the actual data contained in the database must be planned and developed together and executed in a synchronized way. Even under a careful development discipline, the impact of changing an application data model is hard to predict. The lifetime of an application comprises changes and updates designed and tested using data, which is usually far from the real, production, data. So, coding DDL and DML SQL scripts to update database schema and data, is the usual (and hard) approach taken by developers. Such manual approach is error prone and disconnected from the real data in production, because developers may not know the exact impact of their changes. This work aims to improve the maintenance process in the context of Agile Platform by Outsystems. Our goal is to design and implement new data-model evolution features that ensure a safe support for change and a sound migration process. Our solution includes impact analysis mechanisms targeting the data model and the data itself. This provides, to developers, a safe, simple, and guided evolution process

The impact of using pair programming on system evolution a simulation-based study

In this paper we investigate the impact of pair--programming on the long term evolution of software systems. We use system dynamics to build simulation models which predict the trend in system growth with and without pair programming. Initial results suggest that the extra effort needed for two people to code together may generate sufficient benefit to justify pair programming.Peer reviewe

Software systems engineering: a journey to contemporary agile and beyond, do people matter?

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Software systems engineering: a journey to contemporary agile and beyond, do people matter?

It is fascinating to view the evolution of software systems engineering over the decades. At the first glance, it could be perceived that the various approaches and processes are different. Are they indeed different? This paper will briefly discuss such a journey relating to findings from an empirical study in some organisations in the UK. Some of the issues described in the literature and by practitioners are common across different software system engineering approaches over the time. It can be argued that human-element of software development plays an integral part in the success of software systems development endeavour. After all, software engineering is a human-centric craft. In order to understand such issues, we crossed the discipline to other disciplines in order to adapt theories and principles that will help to better understand and tackle such matter. Other disciplines have well established human related theories and principles that can be useful. From Japanese management philosophies, we have adapted Lean and knowledge management theories. From psychology, we have adapted Emotional Intelligence (EI). With such an interdisciplinary view, some of the issues can be addressed adequately. Which bring the question: is it really the process or the people? The second author will reflect on his experience attending the first SQM conference 25 years ago. The reflection will discuss the evolution of software systems engineering, and what was changed since then, if at all changed

What is the Connection Between Issues, Bugs, and Enhancements? (Lessons Learned from 800+ Software Projects)

Agile teams juggle multiple tasks so professionals are often assigned to multiple projects, especially in service organizations that monitor and maintain a large suite of software for a large user base. If we could predict changes in project conditions changes, then managers could better adjust the staff allocated to those projects.This paper builds such a predictor using data from 832 open source and proprietary applications. Using a time series analysis of the last 4 months of issues, we can forecast how many bug reports and enhancement requests will be generated next month. The forecasts made in this way only require a frequency count of this issue reports (and do not require an historical record of bugs found in the project). That is, this kind of predictive model is very easy to deploy within a project. We hence strongly recommend this method for forecasting future issues, enhancements, and bugs in a project.Comment: Accepted to 2018 International Conference on Software Engineering, at the software engineering in practice track. 10 pages, 10 figure