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Blockchain technology is probably the most attractive innovation since the emergence of the internet. Blockchain refers to an open distributed ledger spread across multiple computers that hold digitally recorded transactions in a much more efficient, transparent, and secure manner. A blockchain consists of a number of blocks, each containing data relating to digital assets and a hash header that links it to the previous block in the chain. The blocks are linked together, and new blocks can be added and removed, following a process of consensus. Also, those involved in the transactions can share the distributed digital ledger without needing a centralized intermediary. This chapter aims to introduce blockchain technology and discuss the use cases and initiatives in various sectors of the society, in particular within the blockchain product vendors and service providers. Characteristics, benefits, types, underlying technologies, and platforms are also discussed for the sake of completeness.

Blockchain technology is one of the latest innovations in computer science that has world-wide scale and reach. A blockchain is a mathematical structure for storing digital transactions in a distributed and decentralized digital ledger consisting of multiple blocks that are linked using cryptographic signatures and spread across multiple computers. Each block records digital transactions data securely and has a hash link pointing to the previous block in the network in an efficient manner. New blocks can be added and removed through an agreed consensus of all nodes that each share the distributed ledger without the need for a centralized controlling authority. This chapter aims to introduce the blockchain technology, also known as distributed ledger technology (DLT), and discuss its applications in various sectors of the society including banking, healthcare, transportation, manufacturing, supply chain, and education. Extension of the internet of things vision using the DLT is also briefly discussed.

The presence of bugs in a software release has become inevitable. The loss incurred by a company due to the presence of bugs in a software release is phenomenal. Modern methods of testing and debugging have shifted focus from ‘detecting’ to ‘predicting’ bugs in the code. The existing models of bug prediction have not been optimized for commercial use. Moreover, the scalability of these models has not been discussed in depth yet. Taking into account the varying costs of fixing bugs, depending on which stage of the software development cycle the bug is detected in, this chapter uses two approaches—one model which can be employed when the ‘cost of changing code’ curve is exponential and the other model can be used otherwise. The cases where each model is best suited are discussed. This chapter proposes a model that can be deployed on a cloud platform for software development companies to use. The model in this chapter aims to predict the presence or absence of a bug in the code, using machine learning classification models. Using Microsoft Azure’s machine learning platform, this model can be distributed as a web service worldwide, thus providing bug prediction as a service (BPaaS).

The presence of bugs in a software release has become inevitable. The loss incurred by a company due to the presence of bugs in a software release is phenomenal. Modern methods of testing and debugging have shifted focus from “detecting” to “predicting” bugs in the code. The existing models of bug prediction have not been optimized for commercial use. Moreover, the scalability of these models has not been discussed in depth yet. Taking into account the varying costs of fixing bugs, depending on which stage of the software development cycle the bug is detected in, this paper uses two approaches - one model which can be employed when the 'cost of changing code' curve is exponential and the other model can be used otherwise. The cases where each model is best suited are discussed. This paper proposes a model that can be deployed on a cloud platform for software development companies to use. The model in this paper aims to predict the presence or absence of a bug in the code, using machine learning classification models. Using Microsoft Azure's machine learning platform this model can be distributed as a web service worldwide, thus providing Bug Prediction as a Service (BPaaS).

In recent years, there have been several proposals for amount-of-reuse metrics. However, it is not clear if these metrics measure anything different from other software metrics that are often generally used. Besides, the relationship between these metrics is also unclear. Without a clear understanding, the true added value provided by these metrics remains unknown. In this paper, an analysis of the existing amount-of-reuse metrics is presented and a subset of these is selected, on the basis of their industrial applicability, for further investigation. These metrics are then applied to a collection of different software products to obtain an understanding of the level of correlation that exists between them and other well-known software metrics such as cyclomatic complexity, volume and lines of code. Our analysis suggests that the selected subset of amount-of-reuse metrics provides a relatively independent view of the analyzed code.

Connected Government suggests provisioning of a government's services to its citizens using the Web and communications technologies employing the latest software development paradigms and related methodologies. This also requires appropriate integration of, and interaction between, software applications and e-services developed by various government departments as well as the other influencing sectors of the society such as commerce. This is especially so as the connected government (c-government) applications require open, flexible, interoperable, collaborative, and integrated architecture to provide services for the emerging technologies such as mobile, cloud, and big data. This, in turn, suggests a robust and standard mechanism to develop such applications and services. In this context, Service-Oriented Architecture (SOA) is an attractive approach to adopt. SOA has already been proven successful in providing such a framework for delivering software applications as services with flexibility and multi-platform and multi-channel integration that are necessarily required for c-government application offerings. This chapter provides a discussion of the SOA paradigm and the associated citizen and administrative requirements. The chapter also presents a service-oriented architectural framework based on a set of evaluated application characteristics that support newer technologies. A number of service-component models have also been proposed that provide required customisation, reuse, flexibility, and extensibility. In the context of the proposed overall service-oriented architecture, a large-scale sub-system that the authors term “e-Taxservice” has been used as a case study. The study has a service design that has been validated against a set of key service quality attributes.

In recent years, there have been several proposals for amount-of-reuse metrics. However, it is not clear if these metrics measure anything different from other software metrics that are often generally used. Besides, the relationship between these metrics is also unclear. Without a clear understanding, the true added value provided by these metrics remains unknown. In this paper, an analysis of the existing amount-of-reuse metrics is presented and a subset of these is selected, on the basis of their industrial applicability, for further investigation. These metrics are then applied to a collection of different software products to obtain an understanding of the level of correlation that exists between them and other well-known software metrics such as cyclomatic complexity, volume and lines of code. Our analysis suggests that the selected subset of amount-of-reuse metrics provides a relatively independent view of the analyzed code.

Cloud Computing is a generic term for delivering hosted services over the Internet. It follows a pay-as-you-go approach. Cloud Computing offers numerous benefits for the enterprises, however, there are also many issues, as with any new paradigm or technology. One of the main issues relate to the security and confidentiality of customer data in terms of its location, relocation, availability and security. This paper outlines the Cloud benefits, briefly explains the delivery and deployment models and discusses in detail the issues relating to data in the Cloud. The aim is to provide some useful background information for organizations preparing to migrate to the Cloud to take advantage of this latest computing paradigm.

Internet security is paramount in today’s networked systems, especially when they provide wireless application access and enable personal and confidential data to be transmitted across the networks. Numerous tools and technologies are available to ensure system security, however, external threats to computer systems and applications are also becoming more and more sophisticated. This chapter presents a framework that consists of two components: (1) an assessment model to look at the existing security infrastructure of an organisation to determine its security maturity level; and (2) a process improvement maturity model to suggest an improvement mechanism for the organisation to progress from one maturity level to the next higher level. The intention is to provide a framework to improve the organisation’s Internet and network security so that it becomes more efficient and effective than before. The improvement process model is a 5-stage framework, which has the potential to be established as a standard maturity model for assessing and improving security levels in a manner similar to other software process improvement framework such as CMMI. Preliminary results, based on looking at the existing security measures of one particular organisation, reveal that there is indeed a need for an appropriate model such as the one being proposed in this chapter.

Free riders in the Peer-to-Peer (P2P) networks are nodes that only consume services but provide little or nothing in return. They seriously degrade the fault-tolerance, scalability and content availability of the P2P systems. The solution to free riding problem in P2P networks is to have incentive mechanisms that aim to improve the network utility by influencing the nodes to be more cooperative. This paper proposes five design requirements that an incentive mechanism should satisfy and suggests that the existing incentive mechanisms can be classified into three categories: Monetary-based, Fixed contribution and Reciprocity-based schemes. The paper outlines the principles, provides typical examples and applications and discusses limitations of these schemes. The applicability of each scheme is also discussed against the proposed deign requirements. The aim is to provide an overview and guidance to enhance the applicability of incentive mechanisms in P2P systems.

Database management courses are an essential part of computer science curricula in educational institutions. Courses are normally well-designed, however, in order for students to better understand the theoretical concepts, hands-on practical sessions are also necessary. Such practice will help students to meet the challenges of job market and working in the industry. This paper suggests that group projects be used as a tool to make the teaching and learning of the subject area, more effective. In the paper, we first discuss the design of a course module and describe a pedagogical approach that was adopted to teach a database management systems (DBMS) course at a university in Pakistan and then analyse students’ feedback comments to gauge the effectiveness of the teaching strategy. Our design proposes a detailed 16-week plan for the teaching of the subject. The current study also provides guidelines for the teaching staff intending to take on the responsibilities of delivering DBMS and similar courses.

International audience; Application Service Providers (ASPs) are increasingly adopting the cloud computing paradigm to provision remotely available resources for their applications. In this context, the ability of cloud computing to provision resources on-demand in an elastic manner is of the utmost practical interest for them. As a consequence, the field of cloud computing has witnessed the development of a large amount of elasticity management solutions deeply rooted in works from distributed systems and grid computing research communities. This chapter presents some solutions that differ in their goals, in the actions they are able to perform and in their architectures. In this chapter, we provide an overview of the concept of cloud elasticity and propose a classification of the mechanisms and techniques employed to manage elasticity. We also use this classification as a common ground to study and compare elasticity management solutions.

The surging demand for inexpensive and scalable IT infrastructures has led to the widespread adoption of Cloud computing architectures. These architectures have therefore reached their momentum due to inherent capacity of simplification in IT infrastructure building and maintenance, by making related costs easily accountable and paid on a pay-per-use basis. Cloud providers strive to host as many service providers as possible to increase their economical income and, toward that goal, exploit virtualization techniques to enable the provisioning of multiple virtual machines (VMs), possibly belonging to different service providers, on the same host. At the same time, virtualization technologies enable runtime VM migration that is very useful to dynamically manage Cloud resources. Leveraging these features, data center management infrastructures can allocate running VMs on as few hosts as possible, so to reduce total power consumption by switching off not required servers. This chapter presents and discusses management infrastructures for power-efficient Cloud architectures. Power efficiency relates to the amount of power required to run a particular workload on the Cloud and pushes toward greedy consolidation of VMs. However, because Cloud providers offer Service-Level Agreements (SLAs) that need to be enforced to prevent unacceptable runtime performance, the design and the implementation of a management infrastructure for power-efficient Cloud architectures are extremely complex tasks and have to deal with heterogeneous aspects, e.g., SLA representation and enforcement, runtime reconfigurations, and workload prediction. This chapter aims at presenting the current state of the art of power-efficient management infrastructure for Cloud, by carefully considering main realization issues, design guidelines, and design choices. In addition, after an in-depth presentation of related works in this area, it presents some novel experimental results to better stress the complexities introduced by power-efficient management infrastructure for Cloud.