There is growing interest in the concept of digital twins (DTs) among software engineers and researchers. As an emerging topic, DTs are a promising paradigm to enhance the predictability, safety, and reliability of cyber-physical systems in different domains. Despite the increasing trend, it is continually challenging to decide the best approach to implement DTs. Moreover, to the best of the author's knowledge, it was found that there is a lack of conducted research and no systematic reviews on DTs in the transportation industry, especially in the area of railway systems. Therefore, following the systematic literature review method, this thesis work has identified 363 articles in four digital libraries, of which 60 primary articles were included to address three research questions. The review shows that most of the reviewed articles focus on the railway subarea maintenance and inspection, the DT enabling technology artificial intelligence is the most coupled technology. An in-depth analysis found that most of the articles apply machine learning algorithms and techniques in DTs to detect faults, predict failures, make automated decisions, and monitor health status to optimize railway systems. It was also found that interoperability is the most discussed challenge, where the difficulty is to transmit operational data in real-time and also achieve real-time decision making. Furthermore, the analysis shows several opportunities and advantages of DTs, such as reducing maintenance costs and the positive contribution to a reduction in freight transport by road. Finally, based on the findings of the conducted review, a guideline to support the design of a DT for predictive maintenance in railways in the form of a flowchart is presented and explained.

Blockchain technology has gained significant interest during the previous years and, to some extent, is accredited as a universal solution to enabling online transactions between unknown parties without the need for a central authority. However, as we have yet to see widespread blockchain adoption in government or industrial settings, the question arises: What are the technical challenges hindering blockchain adoption? To this end, a systematic literature review is conducted on 29 academic papers researching software engineering challenges in blockchaintechnology. Initially, the papers are inductively analysed to elucidate and identify the challenges frequently discussed in academic literature. Furthermore, a theoretical framework is produced by drawing from the models mainly used in traditional software development, followed by deductive analysis to map out the software engineering activities related to the challenges. Twenty-four challenges are identified, with 12 relating to smart contracts, 11 to blockchain, and 1 to decentralised applications. The most frequently addressed software engineering activity is design, followed by testing.

As our society becomes more dependent on different types of software systems, it is vital that they operate normally under all types of stress. System resilience is a growing subject within computer science and the tech industry. Resilience can be defined as the persistence of dependability when facing change, for example against failures and attacks. During the last few years, a lot of research has been done investigating how to engineer a resilient system, but there seems to be no work reviewing how to actually measure resilience in a software system. By conducting a systematic literature review (SLR) this thesis aims to identify and evaluate different types of methods and metrics to measure system resilience.

Software engineers and researchers in the field are constantly developing new technologies to manage the complexity of current software systems. There is an increasing need for mechanisms that can deal with dynamics in the systems' environment, goals, and requirements. Self-adaptive software systems are a solution to manage the complexity caused by dynamics or runtime variations. Software reuse is a classical solution to deal with complexity and increase the quality of a system in a systematic and efficient way. Despite the large amount of research on self-adaptation, no systematic study has been found, which surveys and reports the application of reuse methods and techniques for the development of self-adaptive software systems. A systematic analysis of reuse methods and techniques for the development of self-adaptive systems is interesting as it provides useful insights for researchers and practitioners in the self-adaptive area. This study systematically reviews relevant research work published between the years 2000 and 2020 at eight well-known venues on self-adaptation and software engineering. By following the systematic literature review method, 97 studies were reviewed and 40 primary studies identified for addressing the research questions. The main objectives of the review are 1) to collect and analyse the reuse-based methods studied and applied for the design and development of self-adaptive software systems, 2) analyse the challenges in the application of reuse-based methods for the development of self-adaptive software systems. The review shows that most of the analysed studies support reuse with component-based software engineering. The primary studies propose different reuse-based methods to allow faster and simpler development of self-adaptive systems. Furthermore, the analysis shows that the reviewed studies report several challenges related to the configuration process, design, performance and uncertainty in the application of reuse methods for the development of self-adaptive systems.

Software engineers and researchers in the field are constantly developing new technologies to manage the complexity of current software systems. There is an increasing need for mechanisms that can deal with dynamics in the systems’ environment, goals, and requirements. Self-adaptive software systems are a solution to manage the complexity caused by dynamics or runtime variations. Software reuse is a classical solution to deal with complexity and increase the quality of a system in a systematic and efficient way. Despite the large amount of research on self-adaptation, no systematic study has been found, which surveys and reports the application of reuse methods and techniques for the development of self-adaptive software systems. A systematic analysis of reuse methods and techniques for the development of self-adaptive systems is interesting as it provides useful insights for researchers and practitioners in the self-adaptive area. This study systematically reviews relevant research work published between the years 2000 and 2020 at eight well-known venues on self-adaptation and software engineering. By following the systematic litera-ture review method, 97 studies were reviewed and 40 primary studies identi-fied for addressing the research questions. The main objectives of the review are 1) to collect and analyse the reuse-based methods studied and applied for the design and development of self-adaptive software systems, 2) analyse the challenges in the application of reuse-based methods for the development of self-adaptive software systems. The review shows that most of the analysed studies support reuse with component-based software engineering. The pri-mary studies propose different reuse-based methods to allow faster and sim-pler development of self-adaptive systems. Furthermore, the analysis shows that the reviewed studies report several challenges related to the configura-tion process, design, performance and uncertainty in the application of reuse methods for the development of self-adaptive systems.

The present study provides a systematic overview of the most important software requirement prioritization techniques. Software requirement prioritization is a process in software engineering that precludes the actual development of software application programs and deals with assigning priorities to single requirements to define the order of their implementation. The study aims to help researchers and practitioners in deciding about the right technique since each has its advantages and limitations. Compared to the existing reviews, the current one not only captures the most promising techniques but the more general trends behind them. The study utilizes the review protocol method that aims to answer four research questions about the most popular techniques, their taxonomy, their limitations, and involved processes. The empirical data was collected from six databases for scientific manuscripts and put under scrutiny to identify the most relevant and elaborated papers. The results from 53 selected manuscripts and 106 discovered techniques demonstrate that there is evidence of a gradual shift from purely manual elicitation methods to computed-assisted and/or algorithmic ones.

Web services discovery is the process of finding the right Web services that best match the end-users’ functional and non-functional requirements. Artificial intelligence, natural language processing, data mining, and text mining techniques have been applied by researchers in Web services discovery to facilitate the process of matchmaking. This thesis contributes to the area of Web services discovery and recommendation, adopting the Design Science Research Methodology to guide the development of useful knowledge, including design theory and artifacts. The lack of a comprehensive review of Web services discovery and recommendation in the literature motivated us to conduct a systematic literature review. Our main purpose in conducting the systematic literature review was to identify and systematically compare current clustering and association rules techniques for Web services discovery and recommendation by providing answers to various research questions, investigating the prior knowledge, and identifying gaps in the related literature. We then propose a conceptual model and a typology of Web services discovery systems. The conceptual model provides a high-level representation of Web services discovery systems, including their various elements, tasks, and relationships. The proposed typology of Web services discovery systems is composed of five groups of characteristics: storage and location characteristics, formalization characteristics, matchmaking characteristics, automation characteristics, and selection characteristics. We reference the typology to compare Web services discovery methods and architectures from the extant literature by linking them to the five proposed characteristics. We employ the proposed conceptual model with its specified characteristics to design and develop the multi-layer data mining architecture for Web services discovery using word embedding and clustering techniques. The proposed architecture consists of five layers: Web services description and data preprocessing; word embedding and representation; syntactic similarity; semantic similarity; and clustering. In the first layer, we identify the steps to parse and preprocess the Web services documents. Bag of Words with Term Frequency–Inverse Document Frequency and three word-embedding models are employed for Web services representation in the second layer. Then in the third layer, four distance measures, including Cosine, Euclidean, Minkowski, and Word Mover, are studied to find the similarities between Web services documents. In layer four, WordNet and Normalized Google Distance are employed to represent and find the similarity between Web services documents. Finally, in the fifth layer, three clustering algorithms, including affinity propagation, K-means, and hierarchical agglomerative clustering, are investigated to cluster Web services based on the observed documents’ similarities. We demonstrate how each component of the five layers is employed in the process of Web services clustering using random-ly selected Web services documents. We conduct experimental analysis to cluster Web services using a collected dataset of Web services documents and evaluating their clustering performances. Using a ground truth for evaluation purposes, we observe that clusters built based on the word embedding models performed better compared to those built using the Bag of Words with Term Frequency–Inverse Document Frequency model. Among the three word embedding models, the pre-trained Word2Vec’s skip-gram model reported higher performance in clustering Web services. Among the three semantic similarity measures, path-based WordNet similarity reported higher clustering performance. By considering the different words representations models and syntactic and semantic similarity measures, the affinity propagation clustering technique performed better in discovering similarities among Web services.

A semi-systematic literature review of research carried out on the evolution of the concept of Euroscepticism since the term first was reportedly used in 1986 with special focus on articles published around the year before the 2019 European Parliament elections and to this date. The thesis was conducted with a particular interest in gathering more knowledge on using an evidence-based method in political science. The purpose was twofold, therefore: one to see how the concept itself has evolved in research, in terms of definition and salience as well as in terms of measuring and explanatory factors, and the other to see if the method used is appropriate to this purpose. From the data gathered, it can be said that the method is pertinent and relevant when assembling research from a widespread and multifaceted area in terms of geography and content, since it is meant to avoid the pitfalls of ‘picking and choosing’ data. The articles thus uncovered have shown that there is a red thread in research on Euroscepticism, that its context has changed and therefore its content, and that Euroscepticism 2020 is a salient issue.

The purpose of this research is to define which instruments with their characteristics exist to mitigate the communication issues in modern software development as well as the exact issues that emerge during software architecture design with their characteristics and validity. A Systematic Literature Review (SLR) is used as a major method of the research. Also, this thesis presents the vision of an ideal tool as well as an attempt to approbate the findings from the SLR in a small surrogate project of the ‘Battleship’ game while applying usual non-functional requirements from highly-collaborative industry projects. Additionally, we make the emphasis on simplicity and ease of use of the tools as well as the importance of the popularisation and promotion of both found and future tools to facilitate verification and adoption in the industry. The most popular Agile approaches (Scrum, Kanban, Extreme Programming, DevOps) do not specify, how these aspects of planning and documentation have to be managed and there are no existing additional widely-used and accepted tools for this purpose. In this work, a significant amount of attention is paid to the verification of the found tools in practice with feedback. For the tools which are not verified in practice, this work does a comparison against the vision of an ideal tool to understand. This helps to understand whether the given tool can be quickly adopted in practice, if further research is continued, or whether certain changes are required. Unfortunately, this work cannot present any existing tools which conform to our vision and are popularised, heavily verified in practice or relatively widely used in the industry. The closest solutions found were introduced in two industry papers concentrating on repurposing the existing Agile practices to mitigate the planning and documentation issues. During an SLR, a few tools were found, which we believe can be relatively easily taken to verification in practice or promoted to be used in industry on an everyday basis. However, we admit that further research of the found tools, which do not satisfy our requirements, can be used to reevaluate them in future to check for the desired properties again. Furthermore, we define a roadmap for future research for both the search for a tool and the development of a new instrument.

It has become possible to say that the metaverse is a great opportunity for investment and trade, as it provides massive financial returns. The metaverse is the next evolution in social connection and the successor to the mobile internet. Non-fungible tokens represent the ownership of unique items in the metaverse and allow the creator to tokenize things like art, real estate, and collectibles. Trading NFT in the metaverse faces challenges, including security, fraud, and scams. Those challenges have negatively affected the stability of this market. “How can NFT trading in metaverse be improved?” is the main question of this thesis to overcome the challenges. The author performed a systematic literature review to survey and explore the possibility of using technologies to reach the ideal use of NFT in the metaverse. The systematic literature review will guide the researcher to gain more information to evaluate it in the research area. Furthermore, Pointing and identifying the gaps and knowledge needed between the research elements. Three main challenges are presented (identity verification, fraud, and ownership) in areas in which technologies that can provide (flexibility, reliability, accuracy, and performance) can apply. Three dominant solutions, smart contracts, oracle nodes, and blockchain are the study and analysis results to realize the research question and identified problem. The elected technologies show an ability to address challenges in different ways and thus maintain the security and effectiveness of trading operations. Also, the result section mentions other solutions not counting on the dominant solutions. Open issues which provide a ground for future research with practical implementations are also discussed.