Understanding the Relations Between Iterative Cycles in Software Engineering

Iterations are one of the most successful mechanisms in software development to ensure that the resulting system is satisfactory. Due to its strengths, various kinds of iterations have been integrated to software development with varying goals. In this paper, we consider different types of iterations related to software development, including prototyping, incremental development, sprints as in e.g. Scrum, and iterations as defined in Lean Startup. The goal is to understand the relations between the types of iterations, and to find out what kind of similarities and differences they have with each other. As a result, we find that while the goals are different, it is possible for the iterations to coexist, so that one form of iteration is used as a tool to complete the goals of another

Systems biology in animal sciences

Systems biology is a rapidly expanding field of research and is applied in a number of biological disciplines. In animal sciences, omics approaches are increasingly used, yielding vast amounts of data, but systems biology approaches to extract understanding from these data of biological processes and animal traits are not yet frequently used. This paper aims to explain what systems biology is and which areas of animal sciences could benefit from systems biology approaches. Systems biology aims to understand whole biological systems working as a unit, rather than investigating their individual components. Therefore, systems biology can be considered a holistic approach, as opposed to reductionism. The recently developed ‘omics’ technologies enable biological sciences to characterize the molecular components of life with ever increasing speed, yielding vast amounts of data. However, biological functions do not follow from the simple addition of the properties of system components, but rather arise from the dynamic interactions of these components. Systems biology combines statistics, bioinformatics and mathematical modeling to integrate and analyze large amounts of data in order to extract a better understanding of the biology from these huge data sets and to predict the behavior of biological systems. A ‘system’ approach and mathematical modeling in biological sciences are not new in itself, as they were used in biochemistry, physiology and genetics long before the name systems biology was coined. However, the present combination of mass biological data and of computational and modeling tools is unprecedented and truly represents a major paradigm shift in biology. Significant advances have been made using systems biology approaches, especially in the field of bacterial and eukaryotic cells and in human medicine. Similarly, progress is being made with ‘system approaches’ in animal sciences, providing exciting opportunities to predict and modulate animal traits

Evolving a software development methodology for commercial ICTD projects

This article discusses the evolution of a “DistRibuted Agile Methodology Addressing Technical Ictd in Commercial Settings” (DRAMATICS) that was developed in a global software corporation to support ICTD projects from initial team setup through ICT system design, development, and prototyping, to scaling up and transitioning, to sustainable commercial models. We developed the methodology using an iterative Action Research approach in a series of commercial ICTD projects over a period of more than six years. Our learning is reflected in distinctive methodology features that support the development of contextually adapted ICT systems, collaboration with local partners, involvement of end users in design, and the transition from research prototypes to scalable, long-term solutions. We offer DRAMATICS as an approach that others can appropriate and adapt to their particular project contexts. We report on the methodology evolution and provide evidence of its effectiveness in the projects where it has been used

Student teamwork: developing virtual support for team projects

In the 21st century team working increasingly requires online cooperative skills as well as more traditional skills associated with face to face team working. Virtual team working differs from face to face team working in a number of respects, such as interpreting the alternatives to visual cues, adapting to synchronous communication, developing trust and cohesion and cultural interpretations. However, co-located student teams working within higher education can only simulate team working as it might be experienced in organisations today. For example, students can learn from their mistakes in a non-threatening environment, colleagues tend to be established friends and assessing teamwork encourages behaviour such as “free-riding”. Using a prototyping approach, which involves students and tutors, a system has been designed to support learners engaged in team working. This system helps students to achieve to their full potential and appreciate issues surrounding virtual teamwork. The Guardian Agent system enables teams to allocate project tasks and agree ground rules for the team according to individuals’ preferences. Results from four cycles of its use are presented, together with modifications arising from iterations of testing. The results show that students find the system useful in preparing for team working, and have encouraged further development of the system

OntoEng: A design method for ontology engineering in information systems

This paper addresses the design problem relating to ontology engineering in the discipline of information systems. Ontology engineering is a realm that covers issues related to ontology development and use throughout its life span. Nowadays, ontology as a new innovation promises to improve the design, semantic integration, and utilization of information systems. Ontologies are the backbone of knowledge-based systems. In addition, they establish sharable and reusable common understanding of specific domains amongst people, information systems, and software agents. Notwithstanding, the ontology engineering literature does not provide adequate guidance on how to build, evaluate, and maintain ontologies. On the basis of the gathered experience during the development of V4 Telecoms Business Model Ontology as well as the conducted integration of the related literature from the design science paradigm, this paper introduces OntoEng and its application as a novel systematic design method for ontology engineering

Development of an ontology for aerospace engine components degradation in service

This paper presents the development of an ontology for component service degradation. In this paper, degradation mechanisms in gas turbine metallic components are used for a case study to explain how a taxonomy within an ontology can be validated. The validation method used in this paper uses an iterative process and sanity checks. Data extracted from on-demand textual information are filtered and grouped into classes of degradation mechanisms. Various concepts are systematically and hierarchically arranged for use in the service maintenance ontology. The allocation of the mechanisms to the AS-IS ontology presents a robust data collection hub. Data integrity is guaranteed when the TO-BE ontology is introduced to analyse processes relative to various failure events. The initial evaluation reveals improvement in the performance of the TO-BE domain ontology based on iterations and updates with recognised mechanisms. The information extracted and collected is required to improve service k nowledge and performance feedback which are important for service engineers. Existing research areas such as natural language processing, knowledge management, and information extraction were also examined

Complexity models in design

Complexity is a widely used term; it has many formal and informal meanings. Several formal models of complexity can be applied to designs and design processes. The aim of the paper is to examine the relation between complexity and design. This argument runs in two ways. First designing provides insights into how to respond to complex systems – how to manage, plan and control them. Second, the overwhelming complexity of many design projects lead us to examine how better understanding of complexity science can lead to improved designs and processes. This is the focus of this paper. We start with an outline of some observations on where complexity arises in design, followed by a brief discussion of the development of scientific and formal conceptions of complexity. We indicate how these can help in understanding design processes and improving designs

Design synthesis and shape generation

If we are to capitalise on the potential that a design approach might bring to innovation in business and society, we need to build a better understanding of the evolving skill-sets that designers will need and the contexts within which design might operate. This demands more discourse between those involved in cutting edge practice, the researchers who help to uncover principles, codify knowledge and create theories and the educators who are nurturing future design talent. This book promotes such a discourse by reporting on the work of twenty research teams who explored different facets of future design activity as part of Phase 2 of the UK's research council supported Designing for the 21st Century Research Initiative. Each of these contributions describes the origins of the project, the research team and their project aims, the research methods used and the new knowledge and understanding generated. Editor and Initiative Director, Professor Tom Inns, provides an introductory chapter that suggests ways the reader might navigate these viewpoints. This chapter concludes with an overview of the key lessons that might be learnt from this collection of design research activity