Global Software Development: Challen ges and Opportunities in Nigeria

To God be all the glory. I am grateful to our Chancellor, Dr. David Oyedepo and the Management Team of this University led by our amiable VC, Professor C. K. Ayo for allowing me to deliver this public lecture today. Today‘s lecture investigates the possibilities of sub-Saharan Africa as a sourcing destination in the software field. To find out the reasons why sub-Saharan African countries in general and Nigeria in particular are not considered a destination for global software development projects. In the study that led to this lecture, a set of professionals from Europe and Africa were interviewed. Results indicate that there are many disadvantages and difficulties impeding Nigeria from becoming a preferred sourcing destination. The main ones are the absence of a strong software industry and the concerns about legislative, fiscal and commercial premises. On the other hand, it is observed that there are also relevant added values and competitive advantages in Nigeria (English-speaking country, same time zone and cost) and, therefore, it can become a potential target for software development outsourcing in the medium and long term

Optimization of Home Mortgage Mover Predictive Model Applying Geo-Spatial Analysis and Machine Learning Techniques

In the last decade digital innovations and online banking services have significantly changed customers banking preferences and behaviour. Banking industry is going through the changes and developments in the provision of banking services that are affecting the structure and the organization of the bank network. However, private home loan, referred as Home Mortgage hereinafter, continue to remain among the products, that customers prefer to have personal interaction about with professional advisors prior making the decision to apply for the loan with financial institution

A reactive architecture for cloud-based system engineering

PhD ThesisSoftware system engineering is increasingly practised over globally distributed locations. Such a practise is termed as Global Software Development (GSD). GSD has become a business necessity mainly because of the scarcity of resources, cost, and the need to locate development closer to the customers. GSD is highly dependent on requirements management, but system requirements continuously change. Poorly managed change in requirements affects the overall cost, schedule and quality of GSD projects. It is particularly challenging to manage and trace such changes, and hence we require a rigorous requirement change management (RCM) process. RCM is not trivial in collocated software development; and with the presence of geographical, cultural, social and temporal factors, it makes RCM profoundly difficult for GSD. Existing RCM methods do not take into consideration these issues faced in GSD. Considering the state-of-the-art in RCM, design and analysis of architecture, and cloud accountability, this work contributes: 1. an alternative and novel mechanism for effective information and knowledge-sharing towards RCM and traceability. 2. a novel methodology for the design and analysis of small-to-medium size cloud-based systems, with a particular focus on the trade-off of quality attributes. 3. a dependable framework that facilitates the RCM and traceability method for cloud-based system engineering. 4. a novel methodology for assuring cloud accountability in terms of dependability. 5. a cloud-based framework to facilitate the cloud accountability methodology. The results show a traceable RCM linkage between system engineering processes and stakeholder requirements for cloud-based GSD projects, which is better than existing approaches. Also, the results show an improved dependability assurance of systems interfacing with the unpredictable cloud environment. We reach the conclusion that RCM with a clear focus on traceability, which is then facilitated by a dependable framework, improves the chance of developing a cloud-based GSD project successfully

Empowering global software development with business intelligence

Context: Global Software Development (GSD) allows companies to take advantage of talent spread across the world. Most research has been focused on the development aspect. However, little if any attention has been paid to the management of GSD projects. Studies report a lack of adequate support for management’s decisions made during software development, further accentuated in GSD since information is scattered throughout multiple factories, stored in different formats and standards. Objective: This paper aims to improve GSD management by proposing a systematic method for adapting Business Intelligence techniques to software development environments. This would enhance the visibility of the development process and enable software managers to make informed decisions regarding how to proceed with GSD projects. Method: A combination of formal goal-modeling frameworks and data modeling techniques is used to elicitate the most relevant aspects to be measured by managers in GSD. The process is described in detail and applied to a real case study throughout the paper. A discussion regarding the generalisability of the method is presented afterwards. Results: The application of the approach generates an adapted BI framework tailored to software development according to the requirements posed by GSD managers. The resulting framework is capable of presenting previously inaccessible data through common and specific views and enabling data navigation according to the organization of software factories and projects in GSD. Conclusions: We can conclude that the proposed systematic approach allows us to successfully adapt Business Intelligence techniques to enhance GSD management beyond the information provided by traditional tools. The resulting framework is able to integrate and present the information in a single place, thereby enabling easy comparisons across multiple projects and factories and providing support for informed decisions in GSD management.This work has partially funded by the GEODAS-BI (TIN2012- 37493-C03-03) and GEODAS-BC (TIN2012-37493-C03-01) projects from the Ministry of Economy and Competitiveness (MINECO) and the Fondo Europeo de Desarrollo Regional FEDER, SDGear (TSI- 100104-2014-4, ITEA 2-Call 7, co-funded by the Ministerio de Industria, Energía y Turismo dentro del Plan Nacional de Investigación Científica, Desarrollo e Innovación Tecnológica 2013-2016, and the APOSTD grant (APOSTD/2014/064) from the Generalitat Valenciana

Fotogrametría de rango cercano aplicada a la Ingeniería Agroforestal

Tesis por compendio de publicaciones[EN]Since the late twentieth century, Geotechnologies are being applied in different research lines in Agroforestry Engineering aimed at advancing in the modeling of biophysical parameters in order to improve the productivity. In this study, low-cost and close range photogrammetry has been used in different agroforestry scenarios to solve identified gaps in the results and improve procedures and technology hitherto practiced in this field. Photogrammetry offers the advantage of being a non-destructive and non-invasive technique, never changing physical properties of the studied element, providing rigor and completeness to the captured information. In this PhD dissertation, the following contributions are presented divided into three research papers: • A methodological proposal to acquire georeferenced multispectral data of high spatial resolution using a low-cost manned aerial platform, to monitor and sustainably manage extensive áreas of crops. The vicarious calibration is exposed as radiometric calibration method of the multispectral sensor embarked on a paraglider. Low-cost surfaces are performed as control coverages. • The development of a method able to determine crop productivity under field conditions, from the combination of close range photogrammetry and computer vision, providing a constant operational improvement and a proactive management in the crop monitoring. An innovate methodology in the sector is proposed, ensuring flexibility and simplicity in the data collection by non-invasive technologies, automation in processing and quality results with low associated cost. • A low cost, efficient and accurate methodology to obtain Digital Height Models of vegatal cover intended for forestry inventories by integrating public data from LiDAR into photogrammetric point clouds coming from low cost flights. This methodology includes the potentiality of LiDAR to register ground points in areas with high density of vegetation and the better spatial, radiometric and temporal resolution from photogrammetry for the top of vegetal covers.[ES]Desde finales del siglo XX se están aplicando Geotecnologías en diferentes líneas de investigación en Ingeniería Agroforestal orientadas a avanzar en la modelización de parámetros biofísicos con el propósito de mejorar la productividad. En este estudio se ha empleado fotogrametría de bajo coste y rango cercano en distintos escenarios agroforestales para solventar carencias detectadas en los resultados obtenidos y mejorar los procedimientos y la tecnología hasta ahora usados en este campo. La fotogrametría ofrece como ventaja el ser una técnica no invasiva y no destructiva, por lo que no altera en ningún momento las propiedades físicas del elemento estudiado, dotando de rigor y exhaustividad a la información capturada. En esta Tesis Doctoral se presentan las siguientes contribuciones, divididas en tres artículos de investigación: • Una propuesta metodológica de adquisición de datos multiespectrales georreferenciados de alta resolución espacial mediante una plataforma aérea tripulada de bajo coste, para monitorizar y gestionar sosteniblemente amplias extensiones de cultivos. Se expone la calibración vicaria como método de calibración radiométrico del sensor multiespectral embarcado en un paramotor empleando como coberturas de control superficies de bajo coste. • El desarrollo de un método capaz de determinar la productividad del cultivo en condiciones de campo, a partir de la combinación de fotogrametría de rango cercano y visión computacional, facilitando una mejora operativa constante así como una gestión proactiva en la monitorización del cultivo. Se propone una metodología totalmente novedosa en el sector, garantizando flexibilidad y sencillez en la toma de datos mediante tecnologías no invasivas, automatismo en el procesado, calidad en los resultados y un bajo coste asociado. • Una metodología de bajo coste, eficiente y precisa para la obtención de Modelos Digitales de Altura de Cubierta Vegetal destinados al inventario forestal mediante la integración de datos públicos procedentes del LiDAR en las nubes de puntos fotogramétricas obtenidas con un vuelo de bajo coste. Esta metodología engloba la potencialidad del LiDAR para registrar el terreno en zonas con alta densidad de vegetación y una mejor resolución espacial, radiométrica y temporal procedente de la fotogrametría para la parte superior de las cubiertas vegetales

Global analysis of the controls on seawater dimethylsulfide spatial variability

Dimethylsulfide (DMS) emitted from the ocean makes a significant global contribution to natural marine aerosol and cloud condensation nuclei, and therefore our planet&rsquo;s climate. Oceanic DMS concentrations show large spatiotemporal variability, but observations are sparse, so products describing global DMS distribution rely on interpolation or modelling. Understanding the mechanisms driving DMS variability, especially at local scales, is required to reduce uncertainty in large scale DMS estimates. We present a study of mesoscale and sub-mesoscale (&lt;100 km) seawater DMS variability that takes advantage of the recent expansion in high frequency seawater DMS observations and uses all available data to investigate the typical distances over which DMS varies in all major ocean basins. These DMS spatial variability lengthscales (VLS) are uncorrelated with DMS concentrations. DMS concentrations and VLS can therefore be used separately to help identify mechanisms underpinning DMS variability. When data are grouped by sampling campaigns, almost 80 % of the DMS VLS can be explained using the VLS of sea surface height anomalies, density, and chlorophyll-a. Our global analysis suggests that both physical and biogeochemical processes play an equally important role in controlling DMS variability, in contrast with previous results based on data from the low&ndash;mid latitudes. The explanatory power of sea surface height anomalies indicates the importance of mesoscale eddies in driving DMS variability, previously unrecognised at a global scale and in agreement with recent regional studies. DMS VLS differs regionally, including surprisingly high frequency variability in low latitude waters. Our results independently confirm that relationships used in the literature to parameterise DMS at large scales appear to be considering the right variables. However, contrasts in regional DMS VLS highlight that important driving mechanisms remain elusive. The role of sub-mesoscale features should be resolved or accounted for in DMS process models and parameterisations. Future attempts to map DMS distributions should consider the length scale of variability.</p

Agile Software Development Practices in Remote Working Contexts: A Systematic Literature Review

Information Scienc

Automating Inspection of Tunnels With Photogrammetry and Deep Learning

Asset Management of large underground transportation infrastructure requires frequent and detailed inspections to assess its overall structural conditions and to focus available funds where required. At the time of writing, the common approach to perform visual inspections is heavily manual, therefore slow, expensive, and highly subjective. This research evaluates the applicability of an automated pipeline to perform visual inspections of underground infrastructure for asset management purposes. It also analyses the benefits of using lightweight and low-cost hardware versus high-end technology. The aim is to increase the automation in performing such task to overcome the main drawbacks of the traditional regime. It replaces subjectivity, approximation and limited repeatability of the manual inspection with objectivity and consistent accuracy. Moreover, it reduces the overall end-to-end time required for the inspection and the associated costs. This might translate to more frequent inspections per given budget, resulting in increased service life of the infrastructure. Shorter inspections have social benefits as well. In fact, local communities can rely on a safe transportation with minimum levels of disservice. At last, but not least, it drastically improves health and safety conditions for the inspection engineers who need to spend less time in this hazardous environment. The proposed pipeline combines photogrammetric techniques for photo-realistic 3D reconstructions alongside with machine learning-based defect detection algorithms. This approach allows to detect and map visible defects on the tunnel’s lining in local coordinate system and provides the asset manager with a clear overview of the critical areas over all infrastructure. The outcomes of the research show that the accuracy of the proposed pipeline largely outperforms human results, both in three-dimensional mapping and defect detection performance, pushing the benefit-cost ratio strongly in favour of the automated approach. Such outcomes will impact the way construction industry approaches visual inspections and shift towards automated strategies